With startup idea validator as an example:

1. Write an agent specification first. It captures what the agent does, how it behaves,  and how you know it works

User intent 

• Agents JD, what is the agents job

  • Find competitors for a startup idea

  • Identify the wedge and propose what to build first 

  • Replacing messy, self-maanaged prompt chains with one structured,  grounded run that ends in a decision 

•            Target audience

  • Startup founders, or hobby builders trying to validate their ideas. 

• Pain points it addresses

  • Avoids messy prompt sequences, no need for the same long chain or prompt engineering. No structure. 

  • Can't trust the output,  models can mix up links and provide inaccurate information

  • Wall of text and usually no verdict in the end of an llm research ask. 

  • Nothing to share, on clean artifact 

Suggested Prompts (how people interact with the agent)

For each suggested prompt:

• Specify display prompt (user sees and clicks)

• Command prompt that tells agent how to behave 

Capabilities: (define what agent can and cant do)

1. Primary functions 

   1. Find competitors, checks urls,  and ranks them

   2. Compare competitors with differentiation analysis

   3. Identify market gaps/wedges, score them against versioned rubric 

   4. Propose a wedge and first build, then export results into clean output 

      
      

2. Tools and data access 

   1. Groq or gemini for llm extraction, analysis, wedge candidates, and judge panel 

   2. Tavily web search

   3. Various VC / accelerator databases

   4. Optional other APIs like product hunt, gtihub, reddit, news 

   5. Firestore for logged in users, or in-memory 

   6. URL fetch

3. Restrictions 

   1. Cant invent competitors or data

   2. Wont write code or scaffold MVP

   3. Wont write a PRD or product spec

   4. Wont build a business plann or financial model 

   5. Wont do GTM work 

   6. Wont design logos or UI

   7. Wont give legal or fundraising advice 

   8. LLM cant decide the wedge ranking itself, python scored from the rubric (needs review)

   9. Cant follow instructionns in web pages (prompt ingection)

   10. Cant exceed costs or rate limits pas per user quotas or burst limits

       
       

4. Evaluation (how we judge quality)

   1. Rubric: a set of named criteria, each with a description of what a good answer looks like, so different reviewers grade the same response the same way

      1. Consider relevance, roundedness, and tone of output

   2. Shadow rating: runs the agent silently alongside a human so you can compare answers 

5. Test data (data the proves the agent works)

   1. Smoke tests: quick checks to run after small updates

   2. Full automated evaluation: larger dataset that scores the agent at scale

   3. Coverage mix: test questions that reflect full range of real use, not just easy ones. Include commons questions users would ask 

Step 2.  Design the agent.

    Connect the following dots: which model powers it, how you ground it in your data, and how you optimize it to hit the targets in your specification.

1. Choose model

   1. Gpt-4 for friendly product questions (chat compeltion model), natural, conversational text

   2. Reasoning models formathc and complex logic

   3. Choose size,  LLM or SLM

       

2. Ground agent in data 

   Either:

   1. RAG connected to internal data

      1. Catalog of existing competitors and products is constantly growing. 

   2. Fine tuning retrains the model on task specific examples to shape behavior or tone. Good for conistent style and tone. 

      1. Might be a good idea to combine fine-tuning for tone and RAG for current data 

      2. Could fine tune the rag chat so  it answers in the tone I want every time, 

3. Optimize against the rubric

   1. Refine the command prompt, prompt engineering

   2. Measure each version against the evaluation rubric

   3. Example metric: Precision --> fewer irrelevant or incorrect recommendations 

*(create a master database that tracks products found in every search),  for the agent to also reference. 

*Run smoke tests for quick feedback, run a full automation evaluation after making big changes 

GenAIOps

3 cycle loop characterized by a 4th loop which is management  (governance, security, and compliance).

• Explore: Where you define the business need, or use case, and design the architecture, including necessary prompts and models.

• Build: Where you develop the initial application and evaluate it iteratively to reach quality and safety targets.

• Operationalize: Where you deploy the application for real-world use, and deliver reliable and responsible service.