TechRounder Resource Library

TechRounder AI & LLM Command Studio

Look up commands and workflow examples for local model runtimes, cloud APIs, coding agents, deployment, and prompt design.

79 Reference Entries
18 Tools & Platforms
5 Entry Types

Commands and options

Browse commands for Ollama, vLLM, OpenAI APIs, coding CLIs, and deployment tools. Check each tool’s current documentation before using them in production.

Prompt design

Use instruction patterns to make requests clearer and outputs easier to validate.

Workflow examples

Search and copy snippets for common AI engineering tasks.

Serving and operations

Find examples for GPU serving, scaling, and monitoring model deployments.

API compatibility

Compare OpenAI-compatible interfaces with provider-specific request formats.

Reference notes

Entries from three reference sources were combined and duplicates removed.

Command Explorer

Search commands, API features, prompt instructions, and workflow examples.

Prompt Template Blueprints

Use these starting points to make instructions clearer and outputs easier to check.

Code Review Prompt

Find bugs and security issues before merge.

You are a senior reviewer. Analyze the following diff. Return:
1) critical issues
2) medium issues
3) low issues
4) suggested patch snippets
Keep output concise and actionable.

RAG Answer Policy

Constrain answers to supplied context only.

Answer only from provided context. If evidence is missing, reply: 'Not found in provided context.' Include citations by chunk id.

JSON Extraction

Generate deterministic machine-readable output.

Extract entities and output valid JSON matching this schema exactly: {"entity": string, "confidence": number, "source": string }.

Incident Summary

Summarize ops incidents for the operations team.

Summarize this incident in under 120 words with sections: Impact, Root Cause, Mitigation, Next Steps.

Agent Tool Policy

Control tool use in agent workflows.

You may call only these tools: [search_docs, query_db]. Never call external web tools. Ask for confirmation before destructive actions.

Migration Planner

Plan model/vendor migration safely.

Given current stack and target stack, produce a phased migration plan with compatibility gaps, tests, rollback criteria, and observability checks.

Execution Playbooks

Follow these short runbooks for common AI engineering tasks.

Local LLM Bring-Up (Ollama)

  1. Start runtime: ollama serve
  2. Pull model: ollama pull llama3.2
  3. Run prompt test: echo "health check" | ollama run llama3.2
  4. Validate API: curl http://localhost:11434/api/tags

High-Throughput Serving (vLLM)

  1. Install: uv pip install vllm
  2. Serve model: vllm serve <model> --port 8000
  3. Smoke test: curl http://localhost:8000/v1/models
  4. Tune GPU usage: --tensor-parallel-size / --gpu-memory-utilization

Fine-Tune Runbook (OpenAI)

  1. Upload dataset file
  2. Create fine_tuning job
  3. Poll status/events until complete
  4. Switch inference traffic to tuned model after eval pass

Agentic Coding Loop

  1. Open repo context with Claude/Aider/Codex
  2. Generate plan, apply code edits, run tests
  3. Inspect diff and commit with clear message
  4. Run security + lint checks before push

RAG Deployment Checklist

  1. Generate embeddings and build vector index
  2. Apply retrieval filters and chunking policy
  3. Enforce answer-from-context prompt policy
  4. Track retrieval hit-rate and hallucination metrics

LLM Ops Monitoring

  1. Track latency and token usage per request
  2. Monitor GPU memory/utilization continuously
  3. Alert on error spikes and timeout rates
  4. Review logs and eval drift weekly