Collection and structuring of input data
Start with a simple sketch. Write neural networks in your own words, what exactly you need to get out. Do not immediately try to write beautifully - write thesis. Specify the purpose of the project, the target audience and the main limitations. The model perfectly understands even the chaotic flow of thoughts. Ask the AI assistant to ask you clarifying questions to fill in the gaps. This will save you the need to remember all the technical details yourself.
Definition of the role of the performer
In order for the neural network to make a professional TK, specify for whom the document is written. Set models a task: “Write a specification for an experienced web developer” or “for a commercial copywriter”. This will force the algorithm to use the correct terminology, professional slang, and metrics that are understandable to a particular specialist. This approach eliminates the ambiguity of wording and helps the contractor to immediately assess the real amount of work.
Formation of the structure by blocks
Quality TK should consist of clear blocks. Ask the model to break the document into logical sections: general information, requirements for the result, stages of work, acceptance criteria and delivery format. Ask the AI to detail the technical limitations. For example, if it is a TK per text, specify requirements for uniqueness, academic nausea, and keywords. A clear structure helps the performer not to miss important details in the implementation process.
Creation of use cases
For complex IT products or interfaces, TK must contain scenarios of user interaction with the system. Ask the neural network to simulate the behavior of different types of users on your future site or app. Give the command: “Describe the step-by-step path of the client from the moment of access to the landing page to the completion of the target action.” The resulting scenarios will become the basis for the work of designers and testers.
Finalization and validation
The resulting draft TK should be checked for realism and completeness. Ask the same neural network to act as a critical performer. Ask the prompt: "Read this TK from the developer's perspective. What items do you find inaccurate or blurry? What data is lacking to get started right now?” Eliminate the found weaknesses, then draw up the final document for sending to the team.