idea: Self-Improving Prompt Engineering System with MinionS and LangSmith

[sdjc84]

Self-Improving Prompt Engineering System with MinionS and LangSmith

Note: Grok 3 just put a little effort in this, it has not been implemented or tested.

1. Objective

Develop a self-improving system to capture, evaluate, and store prompt engineering refinements for the MinionS protocol using LangSmith, maintaining a prompt_strategies.md file to optimize local (e.g., Ollama with Llama 3.2) and cloud (e.g., GPT-4o) LLM collaboration.

Goals:

• Log prompts and feedback in LangSmith.
• Evaluate prompts for accuracy and efficiency.
• Store high-performing prompts in a .md file.
• Automate updates to the .md file.
• Reuse prompts to reduce iteration.
• Handle edge cases (e.g., missing prompts, API failures).

Success Metrics:

• 90%+ prompt reuse success rate.
• Reduce MinionS communication rounds (e.g., 3 to 1).
• Maintain prompts with >0.9 accuracy/efficiency scores.

2. Tools

• MinionS Protocol (GitHub):
  • Install: pip install torch transformers streamlit
  • Run demo: streamlit run app.py
  • Config: Local LLM (Ollama, Llama 3. Runt, temperature=0.0), Cloud LLM (OpenAI GPT-4o, API key).
  • Code:

    From minions.clients.ollama import OllamaClient
    From minions.clients.openai import OpenAIClient
    From minions.minions import Minions
    Local_client = OllamaClient(model_name=”llama3.2”, temperature=0.0)
    Remote_client = OpenAIClient(model_name=”gpt-4o”)
    Minions = Minions(local_client, remote_client)

• LangSmith (LangSmith):
  • Install: pip install langsmith
  • Config: API key, LANGCHAIN_TRACING_V2=true, LANGCHAIN_PROJECT=MinionS_Prompts
  • Tracing: Wrap OpenAI (wrap_openai), manual logging for Ollama.
• Other Tools: markdown, re libraries (pip install markdown), cron/GitHub Actions for scheduling.

3. Actionable Implementation Steps

3.1 Capture Refinements

• Log prompts during task decomposition:

From langsmith import Client
Client = Client()
Def decompose_task(task, context, remote_client):
    Prompt = f”Decompose the task: {task} into subtasks.”
    Subtask_prompts = remote_client.chat(prompt)
    For I, p in enumerate(subtask_prompts):
        Client.log_run(inputs={“prompt”: p, “task”: task}, outputs={}, project_name=”MinionS_Prompts”, metadata={“subtask_id”: i})
    Return subtask_prompts

• Log local LLM outputs:

Def execute_subtask(subtask_prompt, local_client):
    Output = local_client.generate(subtask_prompt)
    Client.log_run(inputs={“prompt”: subtask_prompt}, outputs={“response”: output}, project_name=”MinionS_Prompts”)
    Return output

• Edge Case: Fallback prompt if API fails: “Summarize key findings.”

3.2 Evaluate Prompts

• Use LangSmith’s LLM-as-Judge:

Def evaluate_prompt(run):
    Expected = “Expected output”
    Actual = run.outputs.get(“response”, “”)
    Score = 1.0 if expected in actual else 0.5
    Return {“score”: score}
Client.evaluate_runs(project_name=”MinionS_Prompts”, evaluator=evaluate_prompt)

• Query top prompts: runs = client.list_runs(project_name=”MinionS_Prompts”, filters={“score”: {“gte”: 0.9}})

3.3 Store Prompts

• Update prompt_strategies.md:

From datetime import datetime
Def update_prompt_md(runs, filename=”prompt_strategies.md”):
    Content = “# Prompt Engineering Strategies\n\n”
    Task_types = {}
    For run in runs:
        Task_type = run.metadata.get(“task_type”, “Unknown”)
        Prompt = run.inputs.get(“prompt”, “”)
        Score = run.scores.get(“score”, 0.0)
        Task_types.setdefault(task_type, []).append((prompt, score))
    For task_type, prompts in task_types.items():
        Content += f”## Task Type: {task_type}\n\n### Effective Subtasks\n”
        For I, (prompt, score) in enumerate(prompts, 1):
            Content += f”{i}. \”{prompt}\”\n   - Score: {score:.2f}\n   - Uses: 1\n   - Updated: {datetime.now().strftime(‘%Y-%m-%d’)}\n\n”
    With open(filename, “w”) as f:
        f.write(content)

• Example .md:

# Prompt Engineering Strategies
## Task Type: Medical Report Analysis
### Effective Subtasks
1. “Extract blood pressure and classify: normal (<120/80 mmHg), elevated (120-139/80-89 mmHg), high (>140/90 mmHg).”
   - Score: 0.95
   - Uses: 10
   - Updated: 2025-07-03

3.4 Reuse Prompts

• Parse .md file:

Import re
Def get_prompts_for_task(task_type, filename=”prompt_strategies.md”):
    Try:
        With open(filename, “r”) as f:
            Content = f.read()
        Pattern = rf”## Task Type: {re.escape(task_type)}\n\n### Effective Subtasks\n(.*?)(?=\n\n## Task Type\Z)”
        Match = re.search(pattern, content, re.DOTALL)
        If match:
            Return re.findall(r”\”(.*?)\””, match.group(1))
        Return []
    Except FileNotFoundError:
        Return []

• Use in MinionS:

Def run_task(task, context, task_type, minions):
    Prompts = get_prompts_for_task(task_type)
    If prompts:
        Output = minions(task=prompts[0], context=[context], max_rounds=1)
    Else:
        Output = minions(task=task, context=[context], max_rounds=2)
        Client.log_run(inputs={“prompt”: task, “task”: task}, outputs={“response”: output[“final_answer”]}, metadata={“task_type”: task_type})
    Return output

3.5 Automate Updates

• Schedule daily updates via cron:

0 0 * * * python update_prompts.py

• Edge Case: Log to temporary JSON if .md write fails:

Import json
Def save_temp_json(runs):
    With open(“temp_prompts.json”, “w”) as f:
        Json.dump([{“prompt”: r.inputs[“prompt”], “score”: r.scores[“score”]} for r in runs], f)

4. Edge Cases and Challenges

• No Stored Prompts: Generate and log new prompts.
• API Failures: Retry with exponential backoff or use fallback prompt.
• Low Scores: Flag runs with scores <0.5 for manual review.
• Scalability: Use GitHub repository for .md file to handle large teams.

5. Next Steps for Resumption

• Verify Setup: Ensure MinionS and LangSmith are configured.
• Test Logging: Run a sample task (e.g., medical report analysis) and verify LangSmith logs.
• Initialize .md File: Create with initial structure.
• Schedule Automation: Set up cron job or GitHub Action.
• Monitor: Check LangSmith dashboard for prompt performance trends.

6. Example Workflow

For a task like “Evaluate cardiovascular risk”:

1. Check prompt_strategies.md for “Medical Report Analysis” prompts.
2. If none, decompose task with cloud LLM, log prompts.
3. Execute subtasks with local LLM, log outputs.
4. Evaluate with LangSmith, store prompts scoring >0.9.
5. Update .md file