Supporting the solving of a technical problem by way of a large language model

Abstract

The invention relates to a computer-implemented method for supporting the solving of a technical problem by way of a first large language model (B), wherein a prompt (E) belonging to the technical problem is made available to the first large language model (B) from a database (A) for storing prompts (E). First information (IA) concerning an interaction of a user with the first large language model (B) is stored as part of the solving of the technical problem and made available to a second large language model (D). The second large language model (D) then develops a newly formulated prompt (E-UP) for the technical problem.

Description

[0001] Description

[0002] Supporting the solution of a technical task through a Large Language Model

[0003] Technical field

[0004] The invention relates to supporting the solution of a technical problem through a Large Language Model.

[0005] Technical background

[0006] Recently, Large Language Models (LLMs) have demonstrated new capabilities and generalizability across a wide range of tasks in numerous domains. As a result, the use of LLMs to support the resolution of technical problems and issues in industrial contexts is becoming increasingly popular.

[0007] Due to their understanding of natural language, they are suitable for many industrial applications. For example, industries that analyze large amounts of data to gain technical insights, make predictions, or recognize patterns can benefit from the use of LLMs.

[0008] An LLM (Language Learning Model) is an artificial intelligence in the form of a machine learning model trained to understand and generate natural language. LLMs are capable of solving various linguistic tasks. Examples of currently known LLMs include GPT (Generative Pretrained Transformer) models, such as GPT-3 and GPT-4, developed by OpenAI and used in ChatGPT and Microsoft Copilot, as well as Google's BERT (Bidirectional Encoder Representations from Transformers). Such language models have the ability to generate language for general-purpose purposes.

[0009] The user interacts with the LLM by entering prompts. These are text inputs that the model uses to generate a response. A prompt can be a question, a request, a description, or any form of text that tells the model what is desired. The LLM's output responds to or builds upon the given context. The quality of the prompt has a significant impact on the model's output. A well-formulated prompt typically leads to a precise and appropriate response, while an unclear or contradictory prompt may result in unsatisfactory or even incorrect outcomes.

[0010] Summary and embodiments of the invention

[0011] The invention is based on the objective of demonstrating a computer-aided method for supporting the solution of a technical problem by means of a Large Language Model.

[0012] This problem is solved by a method with the features of claim 1. Furthermore, the invention relates to a corresponding device or system for data processing, a corresponding computer program, a corresponding computer-readable, preferably non-volatile, storage medium, and a corresponding transmission signal. Advantageous embodiments and further developments are the subject of dependent claims.

[0013] The computer-implemented method supports the solution of a technical task using a first Large Language Model. For this purpose, a prompt relevant to the technical task is retrieved from a database of prompts and provided to the first Large Language Model. Initial information regarding a user's interaction with the first Large Language Model during the task solution process is stored. This initial information is then provided to a second Large Language Model. The second Large Language Model then receives a newly formulated prompt related to the technical task.

[0014] An example of a technical task is the evaluation of a PPCL code (PPCL: Powers Process Control Language) for building automation.

[0015] Other examples include technical tasks in a wide variety of industries, such as the analysis of serialized piping and instrumentation diagrams (P&IDs) to better understand a plant described in a diagram in the process industry, or the analysis of circuit diagrams in electrical engineering, or the analysis of equipment manuals and maintenance records in mechanical engineering. The method is particularly suitable for recurring technical tasks, i.e., when different users have the same technical question. In this case, it is beneficial to develop an improved prompt that allows the technical task to be solved quickly using the first Large Language Model.The effort invested in developing the improved prompt is therefore worthwhile, since it can be assumed that this prompt will be used by multiple users, and that it will enable users to receive satisfactory answers from the first Large Language Model. Otherwise, each user would individually attempt to create a prompt that would at least result in their question being answered by the LLM after several follow-up questions.

[0016] The automatic generation of the improved prompt is based on initial information regarding a user's interaction with the first Large Language Model while solving the technical task. This interaction includes user input and output from the first Large Language Model. Based on this communication history, conclusions can be drawn about how the originally used prompt could be improved to achieve a satisfactory result more quickly.

[0017] To obtain a newly formulated prompt, the initial information regarding an interaction is provided to a second Large Language Model. This model then generates an improved prompt and outputs it.

[0018] In addition to the prompt, the first Large Language Model can be provided with a document to which the technical task refers. This is advantageous because different users may have the same technical question, but it may arise in different contexts. This different context can then be expressed through the document. For example, the document could be a software section. Preferably, the document is not stored in the database that stores the prompts. In this case, the database contains the prompts for the technical tasks, which are used by every user. Additionally, a user can personalize the technical task by adding the document from their own storage, thus supplementing the context for the technical task.The prompt associated with a technical task can be replaced in the database with the newly formulated prompt. If a user later searches for a prompt for the same task, they can then immediately access an improved prompt compared to the original one and present it to the first Large Language Model.

[0019] The initial stored information may include:

[0020] - the prompt pertaining to the technical task,

[0021] - an output of the first Large Language Model in response to the prompt,

[0022] - at least one piece of additional information provided to the first Large Language Model for this output,

[0023] - at least one further edition of the first Large Language Model that points to at least one additional piece of information.

[0024] Preferably, the stored initial information contains the complete communication between the user and the first Large Language Model. This complete communication begins with the initial prompt provided to the first Large Language Model and ends with a final output from the first Large Language Model. In between, there are one or more user inputs and corresponding outputs from the first Large Language Model.

[0025] Furthermore, secondary information can be stored regarding a user's quality assessment of the first Large Language Model's solution to the technical task. This assessment can refer to the last output of the first Large Language Model and, if applicable, also to the interaction that occurred up to that last output. The quality assessment makes it possible to compare different pieces of the initial information.

[0026] For the same technical task, multiple pieces of initial information can be stored, and a subset of these pieces of initial information can be made available to the second Large Language Model. Using only a subset of the initial information to create an improved prompt has the advantage of leveraging particularly effective interactions to enhance the prompt.

[0027] Regarding the same technical task, multiple pieces of first and second information can be stored. A subset of these pieces of first information can then be selected, taking into account the corresponding second information, and this subset can be made available to the second Large Language Model. In this case, the second information enables a suitable selection of the first information to be used as the basis for developing an improved prompt.

[0028] A selection of multiple technical tasks can be offered, each with a corresponding prompt stored in the database. Upon receiving a selection, the prompt corresponding to the chosen task can be provided to the first Large Language Model. For example, a user can be shown, via a selection menu, which technical tasks are addressed by the prompts contained in the database. From the user's perspective, this provides a collection of technical tasks for which they do not need to formulate a prompt themselves if they wish to use a Large Language Model to solve the task.

[0029] The second Large Language Model can be provided with initial information, along with instruction information for generating the newly formulated prompt. This instruction information corresponds to a prompt for the second Large Language Model. This prompt instructs it to output an improved prompt for solving the technical task.

[0030] The first and second Large Language Models can be the same Large Language Model. In particular, a Large Language Model can be used that is applicable for a wide variety of purposes and / or generally accessible.

[0031] The device or system for data processing includes means for carrying out the described method. In particular, it may include a user interface for the selection of the technical task by a user and for the user's interaction with the first Large Language Model, as well as an interface for communication between the first Large Language Model and the second Large Language Model, and the database for storing prompts.

[0032] The method according to the invention and / or one or more functions, features, and / or steps of the method according to the invention and / or one of its embodiments can be executed using a computer. For this purpose, hardware components and / or software components can be used. It can, for example, be executed or implemented using one or more computers, processors, application-specific integrated circuits (ASICs), digital signal processors (DSPs), and / or so-called field-programmable gate arrays (FPGAs). It can also be executed, at least partially, in a cloud and / or in an edge computing environment. One or more interacting computer programs can be used, wherein a computer program can, in particular, be a software module, a software library, a single procedure, a subroutine, a function, or any other type of code that implements the respective function.When multiple programs are used, they can be stored and executed together on one computer, or on different computers in different locations. Since this is functionally equivalent, the singular forms "the computer program" and "the computer" are predominantly used.

[0033] Exemplary embodiments of the drawing

[0034] Fig. 1 shows a process for creating improved task-specific prompts, Fig. 2 shows a first system suitable for implementing the process shown in Figure 1, and Fig. 3 shows a second system suitable for implementing the process shown in Figure 1.

[0035] Detailed description of the exemplary implementations

[0036] When using a learning management model (LLM), the quality of its output is highly dependent on the quality of the input, i.e., the user's prompts provided to the LLM. Prompt engineering focuses on formulating suitable text inputs that guide the model as effectively as possible to perform the desired tasks or generate the desired outputs. Prompt engineering can involve providing additional context, such as defining expressions, reframing the question, or specifying parameters for the answer. The goal is to optimize the interaction with the model so that the most relevant and accurate answers are generated.

[0037] When prompt engineering is to be carried out in an industrial context, the following difficulties are encountered in particular:

[0038] In addition to knowledge of the technical field relevant to the problem, effective prompt engineering also requires an understanding of how the LLM works. Constantly improving and optimizing prompts manually is labor-intensive and time-consuming, thus diverting valuable resources from other purposes.

[0039] - Language learning management systems (LLMs) typically adapt to developments in language and user behavior. Prompt engineering must also take these changes into account.

[0040] The following describes an automated approach to improving task-specific prompts for learning management assistants (LLMs). A user can then provide this improved task-specific prompt to the LLM to receive a satisfactory response. The improved task-specific prompt is determined by the LLM, which is provided with several examples of successful interactions between a user and an LLM. These successful interactions all relate to the same task, with the user providing additional instructions, clarifications, or context in each instance. The situation addressed by this approach with the improved task-specific prompt is that the user knows what information they want from the LLM but cannot adequately articulate this in a prompt.

[0041] This approach of automatically generating an improved, task-specific prompt can be called prompt prediction. In simpler terms, this means that a learning management assistant (LLM) is asked how the question should have been phrased originally to achieve the desired result, without requiring any further instructions. This prompt prediction can be achieved by providing examples of interactions that resulted in the LLM successfully completing a specific task. The task was the same in each of the successful examples, with the user providing additional information during the interactions.

[0042] Figure 1 shows a process for creating improved task-specific prompts.

[0043] In step S1, the user selects the task they wish to complete. This is a domain-specific task. Prompts for these tasks are stored in database A. The user does not need to formulate the prompt for their relevant domain-specific task themselves; rather, the task-specific prompt E is generated by database A after the user selects the domain-specific task. As an example, consider building automation for a smart building as the technical domain within which the user wishes to complete a task. The user has a PPCL code. This type of code is typically used by building automation technicians to control and adjust various functions of a building control system.PPCL code can therefore be used to program various aspects of building control, such as responding to specific events, controlling lighting, heating, ventilation, and air conditioning, monitoring sensors, etc. Instead of having to analyze this code themselves, the user would prefer to consult a Learning Management Assistant (LLM) for the solution to their related question. For example, a simple question from the building automation technician might be which sensors are installed in a building complex.

[0044] The following are specific examples of task-specific prompts related to a PPCL code:

[0045] Example 1: FDD rules - the user searches for FDD rules that underlie the PPCL code

[0046] The task-specific prompt for this could be:

[0047] Using the attached PPCL code, which specifies the control system of a smart building, create a set of FDD rules. In short, FDD rules define specific expected conditions for building systems and clear limits or performance ranges. Please include only FDD rules that are implemented in the code. For background information, consider the following explanation and examples of FDD tools and rules:

[0048] Fault Detection and Diagnostics (FDD) rules are a set of guidelines for identifying and diagnosing faults in HVAC systems (Heating, Ventilation, and Air Conditioning). These rules are based on the investigation of faults in HVAC systems and the development of hierarchical relationships between different devices and processes within the system. The core of FDD lies in understanding the relationships between "source" and "load," which can be mediated by air or water.

[0049] Fault Detection and Distribution (FDD) rules are applied to various hierarchical relationships to collect fault reports and raw data. Examples of these relationships include a chiller supplying chilled water to ventilation units and a ventilation unit supplying fresh air to end devices. The application of FDD rules varies depending on the system configuration, such as the presence of a chiller, boiler, ventilation units, and end devices, as well as the state of the equipment (e.g., off, startup, or steady state).

[0050] Different sets of FDD rules are applied for specific scenarios, such as staged heating and cooling directly at the air handling unit or single-zone air handling units. Using these rules allows for better maintenance and optimization of HVAC systems, resulting in improved performance and energy efficiency.

[0051] Three examples of FDD rules are:

[0052] 1. A rule for detecting insufficient cooling capacity: This FDD rule monitors the supply and return temperatures of chilled water in a chiller system. If the difference between the supply and return temperatures (also called Delta-T) is consistently lower than expected, this may indicate a problem with the chiller's cooling capacity. The rule can help identify problems such as fouling in the heat exchanger, low refrigerant charge, or poor compressor performance.

[0053] 2. A rule for detecting faulty airflow in ventilation units: This FDD rule compares the actual airflow in an air handling unit (AHU) with the expected or required airflow. If the actual airflow rate is consistently lower or higher than the expected value, this may indicate problems such as clogged filters, a malfunctioning fan, or improper control of the variable air volume (VAF) system. This rule helps ensure that the ventilation system provides adequate ventilation and maintains the desired indoor air quality.

[0054] 3. A rule for identifying faulty sensors: This FDD rule checks for inconsistencies in sensor readings, such as those from temperature, humidity, or pressure sensors within the HVAC system. If a sensor reading deviates significantly from the expected value or exhibits erratic behavior, this may indicate a faulty sensor or a problem with sensor calibration. Identifying and correcting faulty sensors is crucial for maintaining proper control and optimizing the performance of HVAC systems.

[0055] Example 2: Point names - the user searches for point names such as temperature sensors, occupancy sensors, light controls, HVAC equipment, etc., which fulfill a technical function in the PPCL code.

[0056] The task-specific prompt for this could be: Using the attached PPCL code, which specifies the control system of a smart building, extract a list of all point names and a description of their functions. The output should be in the form of a bulleted list, with each bullet point representing a different point name, followed by a description of its function. The following guidelines may be helpful when extracting the point names.

[0057] The following rules and conventions apply when writing PPCL scripts: Point names that begin with numbers must be preceded by the at sign (@).

[0058] APOGEE PPCL programs use an assigned name. Valid names can contain 1 to 30 characters, including: AZ, az, 0-9, spaces ( ), periods (.), commas (,), hyphens (-), underscores (_), and apostrophes (').

[0059] Point names used in programs should be descriptive and describe the function for which they are used. For example, a point name that monitors the outside air temperature could be named OATEMP. When used in PPCL, the calculated value is determined and assigned to a point name or local variable for later reference. Trace the use of local variables up to the final control decision and the point name where the local variable is translated.

[0060] - Use comments to provide additional information: Use comments, if necessary, to provide more context or explanation for a particular point. This can help clarify the purpose of a point without making the point name excessively long. If lines of code are commented out, do not use this information to assign descriptions to points.

[0061] The DEFINE statement allows the user to define abbreviations for (parts of) point names that will be used throughout the script. The syntax is DEFINE(abbrev, x), where "abbrev" is the abbreviation to be used in other PPCL statements for the actual string "x".

[0062] - Use the position of points in statements to refine the options for the function tag. For example, if a point is in the control variable slot of a loop statement, it must be a setpoint, a loop output, or a command signal (binary or analog) and cannot be a temperature, position, or status point. Use the value ranges in loop, table, or set statements to determine whether the point is analog or binary, and use this information to determine the function tag.

[0063] Example 3: Operation sequence - the user searches for the order of operations as represented in the provided PPCL code.

[0064] The task-specific prompt for this could be:

[0065] Using the attached PPCL code, which specifies the control system of a smart building, derive the sequence of operations and write down the sequence of operations in both bullet point style and paragraph style (refer to the following guidelines).

[0066] Please only include operations that are implemented in the code.

[0067] In principle, the operational sequence should outline the engineer's fundamental intentions regarding how the mechanical system(s) should function, how they are designed for operation, and how they should be controlled to achieve this goal. The sequence should be a direct reflection of the mechanical design. An operational sequence that fails to convey the engineer's fundamental design intentions will only complicate the procurement process and the design and submission processes for the control systems.

[0068] A typical sequence of operations usually takes the form of one of two styles: bullet point style or paragraph style. A sequence written in bullet point style would look like this:

[0069] 1. The programmable thermostat indicates the occupancy mode.

[0070] 2. The supply air fan of the roof unit runs continuously.

[0071] 3. The thermostat switches between heating and cooling as needed to maintain the desired temperature in the living space.

[0072] The same sequence written in paragraph style would look like this:

[0073] When the programmable thermostat indicates occupancy mode, the roof unit's supply air fan runs continuously, and the thermostat switches the heating and cooling as needed to maintain the set temperatures for the occupied space. Specified sequences will be available in long or short form, depending on the individual technician's requirements and objectives. A short form of the sequence for a thermostatically controlled exhaust fan might be as follows: the generator room's exhaust fan is to be controlled by a thermostat mounted in the room.

[0074] Example 4: Network structure - the user searches for the logical connection between the technical components that play a role in the code.

[0075] The task-specific prompt for this could be: Specify the network structure for the attached PPCL code that specifies the control system of a smart building.

[0076] Convert the network structure to networkx json, using "nodes" for nodes where each node has an integer ID and a label, and using "link" for edges. If there is insufficient information to create the diagram, convert all identified point names to nodes in the diagram and write nothing else.

[0077] For example, if two point names %A1%HSA and %A1%HSA are extracted without a connection between them, create a graph with two nodes but no edges. Write nothing other than the JSON graph!

[0078] Specifically: Graph = {'directed': True, 'multigraph': False, 'graph': {}, 'nodes': [{'id': '%A1%HSA'}, {'id': '%A1%HSA '}], 'links': []}

[0079] The user can access database A via a human-machine interface (HMI). For example, they can view a dropdown list displaying a summary of domain-specific tasks, allowing them to select the appropriate task. Once selected, the corresponding task-specific prompt E is provided by database A.

[0080] In step S2, the user uploads the input document F, which provides the context in which the domain-specific task is to be solved. In the examples listed above, the input document F is in each case a PPCL code or code snippet.

[0081] In step S3, the output prompt G, consisting of the combination of the input document F and the task-specific prompt E, is provided to the LLM B as input. The LLM B can be any Large Language Model that the user wants to use to solve their domain-specific task.

[0082] The LLM B generates the response to the initial prompt G and outputs it to the user in step S4. If the user is not satisfied with the LLM B's output, they can optionally interact with the LLM B in step S4 by providing additional context or instructions and receiving the corresponding responses. If the user is satisfied with the LLM B's output and has thus received the final result, they rate their satisfaction with this LLM B in step S5 using the feedback information FB. In the simplest case, the feedback information FB can be a rating of Good / Bad, or a single numerical value on a predefined scale. Preferably, the user answers a series of questions, the answers to which then constitute the feedback information FB. For example, the following questions could be used:

[0083] Did the LLM's answer answer your question? Answer on a scale of 1-5.

[0084] Did you save time by using the LLM? Answer on a scale of 1-5.

[0085] Are the LLM's answers technically correct? Answer on a scale of 1-5.

[0086] - How easy was it to correct / re-instruct the LLM? Answer on a scale of 1-5.

[0087] - What did you have to do to make the LLM's answers satisfactory? Answer in free text.

[0088] In step S6, the feedback information FB and the interaction history IA between the user and the LLM B—that is, all inputs and outputs starting with the initial prompt G and ending with the final response from the LLM B—are transferred to the interaction database C. The interaction database C collects and stores completed interactions between users and the LLM B. The information stored here for completed interactions includes, for each interaction, a description of the domain-specific task, the input document F uploaded by the user, the interaction history IA, and the feedback information FB.

[0089] If the interaction database C contains a sufficient number of completed interactions for a specific domain-specific task, or after a certain period of time, such as one month, these interactions, or a selection thereof, are passed to the LLM D in step S7. If only a selection of completed interactions is passed, this selection is made using the respective feedback information FB. It is therefore advisable to forward the completed interactions rated as best according to the feedback information FB. For example, the top 10% of completed interactions can be used. If the feedback information FB comprises multiple values, as shown in the example above, these values ​​must be combined to make different feedback information FB comparable. The LLM D can be any Large Language Model, which can also be the same as the LLM B.The LLM D is intended to solve the task of providing an improved task-specific Prompt E-UP compared to the task-specific Prompt E.

[0090] In step S8, the LLM D is provided with prompt L, which instructs it to determine a new version of the task-specific prompt E using the completed interactions IA received from the interaction database C. Prompt L could, for example, be formulated as follows: considering the completed interactions, how should the task-specific prompt E have been formulated to produce a satisfactory answer for the user with little or no interaction between the user and the LLM? Prompt L only needs to be generated once and can be used every time a new version of a task-specific prompt is requested. The improved task-specific prompt E-UP is then determined by the LLM D and output in step S9.The improved task-specific prompt E-UP is passed to database A in step S10, where it replaces the task-specific prompt E previously stored there.

[0091] If the task-specific prompt E is not a good starting point for solving the task, the interaction between the user and the LLM B may be terminated before a satisfactory response from the LLM B is available. In this case, no feedback information FB is available for the respective interaction. This failed interaction can still be stored in the interaction database C, but it will be marked as "unsuccessful." In this case, the respective task-specific prompt E should also be marked as "unsuccessful" and added to a list of tasks whose task-specific prompts need improvement.

[0092] The described approach helps users initiate a question to a Learning Management Assistant (LLM) using a better prompt. Often, users know what kind of answer they want or what characteristics the LLM's output should have, but struggle to articulate this precisely. Without these precise words, reflected in the prompt, the LLM's output will not meet the user's requirements. Because the creation of the improved task-specific prompt (E-UP) takes into account completed interactions in which users have already elicited a satisfactory output from the LLM, it can be assumed that when the improved task-specific prompt (E-UP) is entered, the LLM's initial response will already be satisfactory, or at least that little interaction between the user and the LLM will be required to arrive at a satisfactory answer.The provision of the precisely formulated prompt as a starting point is not done by the user by "refining" their question, but rather automatically by a computer program.

[0093] It should be emphasized that no task-specific training data is required to generate the improved task-specific prompt E-UP. Instead, it leverages the existing knowledge of domain experts, which is reflected in completed interactions and the technicians' feedback indicating their satisfaction with the LLM.

[0094] Another advantage is that the improved task-specific prompts E-UP are formulated in natural language. They can therefore be easily checked for plausibility, meaningfulness, and quality.

[0095] Figure 2 shows how a system SYS can be structured to implement the procedure described in Figure 1. While the components described in more detail below are shown simply in the figure, they can also be present in multiple instances. Furthermore, while the components described in more detail below are depicted as enclosed in a box in the figure, this does not mean that these components must be located in the same place; rather, it can be a distributed system. In this way, the functionality of the system SYS can be divided among several systems or components, which may be hierarchically linked.

[0096] The SYS system comprises a computing unit or processor PRO, as well as a memory MEM in which a computer program PROG is stored. Furthermore, the memory MEM also includes the databases shown in Figure 1, or alternatively, can access them. The memory MEM is preferably a non-volatile, computer-readable data storage medium. Storage can be carried out in any way suitable for ensuring readability by a computing unit, such as magnetic storage (e.g., using a floppy disk), optical storage (e.g., using a CD), magneto-optical storage, ROM (Read Only Memory), RAM (Random Access Memory), EPROM (Erasable Programmable Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), or flash memory.

[0097] The HMI user interface allows the SYS system to exchange information with a user and provides the user with a graphical user interface for this purpose. Furthermore, an IN / OUT input / output unit is available, enabling the exchange of information between the SYS system and other components, particularly LLMs. The HMI and IN / OUT user interfaces can also be used for user interaction with the LLM. Both interfaces can be configured appropriately, for example, wirelessly or via cable, and communication can be carried out using suitable standards.

[0098] By executing the instructions of the PROG program in the PRO processor, the steps of the procedure described above are carried out.Specifically, the task-specific prompt E is selected from the previously created and stored task-specific prompts and provided to an LLM along with the input document; the user's interaction with the LLM up to the LLM's final response is stored; the user's feedback on satisfaction with the LLM's responses is also stored and used to select completed interactions, which are then provided to an LLM to generate an improved task-specific prompt; this LLM is prompted, using a suitable prompt, to output the improved task-specific prompt; this improved prompt is stored to be provided to the LLM the next time a user requests to solve the same domain-specific task.

[0099] From the user's perspective, the SYS system is a product capable of solving a range of domain-specific tasks. The user selects one of these preconfigured tasks and adds their own input document. They then receive a response and can optionally improve it by providing further instructions or asking follow-up questions. After receiving the final response, they provide feedback on their satisfaction to help the system generate a better prompt for the next user who wants to solve the same domain-specific task with the same or a different input document. Figure 3 shows how another system for implementing the procedure described in Figure 1 can be structured. In this embodiment, the deployment device BE stores a computer program PROG, which contains program instructions for executing the described procedure.The provisioning facility BE is connected to a computer network or the internet N. A computer system COMP or a mobile device such as a smartphone SP can download the computer program PROG via network N and execute the procedure by carrying out the program instructions of the computer program PROG. Alternatively, the computer system COMP or the mobile device SP can instruct the provisioning facility BE via network N to execute the procedure, after which the provisioning facility BE makes the corresponding outputs and / or results available to the computer system COMP or the mobile device SP.

[0100] In one variant of this embodiment, the provisioning device BE can be a computer-readable storage medium, such as an SD card, which stores the computer program PROG and can be connected directly to the computer system COMP or the mobile device SP without the intermediary of the network N.

[0101] The invention has been described above using one exemplary embodiment. It is understood that numerous changes and modifications are possible without departing from the scope of the invention.

Claims

Patent claims 1. Computer-implemented method for supporting the solution of a technical task by a first Large Language Model (B), in which a prompt (E) relating to the technical task is provided to the first Large Language Model (B) from a database (A) for storing prompts (E), initial information (IA) concerning a user's interaction with the first Large Language Model (B) in the context of solving the technical task is stored, the initial information (IA) is provided to a second Large Language Model (D), and a newly formulated prompt (E-UP) relating to the technical task is received from the second Large Language Model (D).

2. The method of claim 1, wherein the technical problem relates to the evaluation of a PPCL code for building automation.

3. Method according to claim 1 or 2, wherein the first Large Language Model (B) is provided with a document (F) in addition to the prompt (E), to which the technical problem relates.

4. Method according to one of claims 1 to 3, wherein in the database (A) the prompt (E) belonging to the technical problem is replaced by the newly formulated prompt (E-UP).

5. Method according to any one of claims 1 to 4, wherein the stored first information (IA) comprises: - the prompt (E) belonging to the technical task, - an output of the first Large Language Model (B) in response to the prompt (E), - at least one additional piece of information provided to the first Large Language Model (B) for this output, - at least one further output of the first Large Language Model (B) which indicates at least one additional piece of information.

6. Method according to any one of claims 1 to 5, wherein second information (FB) is stored concerning a quality assessment by the user relating to the solution of the technical problem by the first Large Language Model (B).

7. A method according to any one of claims 1 to 6, wherein, with respect to the same technical problem, a plurality of first information (IA) is stored and a subset of the plurality of first information (IA) is made available to the second Large Language Model (D).

8. A method according to any one of claims 1 to 7, wherein, with respect to the same technical problem, a plurality of first (IA) and second (FB) information is stored, a subset of the plurality of first information (IA) is selected taking into account the respective associated second information (FB), and the subset of the plurality of first information (IA) is made available to the second Large Language Model (D).

9. Method according to any one of claims 1 to 8, wherein a plurality of technical tasks are made available for selection, for each of which a prompt (E) is contained in the database (A), and after receiving a selection information, the prompt (E) belonging to the selected task is made available to the first Large Language Model (B).

10. Method according to any one of claims 1 to 9, wherein the first information (IA) is provided to the second Large Language Model (D) together with a request (L) to create the newly formulated prompt (E-UP).

11. Method according to any one of claims 1 to 10, wherein the first Large Language Model (B) and the second Large Language Model (D) are the same Large Language Model.

12. Device or system for data processing (SYS), comprising means for carrying out the method according to any one of claims 1 to 11.

13. Device or system for data processing (SYS) according to claim 12, comprising a user interface (HMI) for the selection of the technical task by a user and for the interaction of the user with the first Large Language Model (B), an interface (IN / OUT) for communication with the first Large Language Model (B) and the second Large Language Model (D), the database (A) for storing prompts (E).

14. Computer program (PROG) comprising instructions which, when the program is executed by a computer, cause it to perform the steps of the method according to any one of claims 1 to 11.

15. Computer-readable storage medium (MEM, BE) with a computer program (PROG) according to claim 14.

16. Transmission signal that transmits the computer program (PROG) according to claim 14.

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