TechRounder Resource Library

TechRounder AI & LLM Command Studio

A practical command and instruction reference for local LLM runtimes, cloud APIs, coding agents, deployment, and prompt engineering workflows.

79 Reference Entries
18 Tools & Platforms
5 Entry Types

Command Accuracy

Production-safe commands across Ollama, vLLM, OpenAI APIs, coding CLIs, and deployment stacks.

Prompt Clarity

Instruction design patterns that reduce ambiguity and improve structured outputs.

Workflow Speed

Copy-ready snippets and fast filtering for daily AI engineering tasks.

Infra Readiness

Operational patterns for GPU serving, scaling, and observability.

Tool Interop

OpenAI-compatible and vendor-specific flows mapped for easier migration.

Reference Quality

Merged and deduplicated from three independent cheat-sheet sources.

Command Explorer

Search commands, API primitives, instruction directives, and workflow actions.

Prompt Template Blueprints

Reusable instruction patterns for safer and more consistent AI outputs.

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 stakeholders.

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

Quick operational 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