AI Ml

AI/ML Feature & Product Development

When to use

• Integrating an LLM (chat, completion, structured output, tool calling) into any application
• Building a RAG (Retrieval-Augmented Generation) pipeline
• Designing or running evals for an AI feature
• Adding guardrails, moderation, or safety filters
• Fine-tuning or instruction-tuning a model
• Serving a model in production (batch inference or real-time)
• Building an AI agent with tool use / function calling
• Classical ML: training, feature engineering, experiment tracking, deployment

Workflow

1. Choose the right AI approach before writing code:

   Need	Recommended approach
   General Q&A / summarization	Prompt-engineered API call (cheapest, fastest)
   Grounding in private docs	RAG over a vector store
   Consistent structured output	LLM + JSON schema / function calling
   Domain-specific style or facts	Fine-tuning (only if RAG + prompting fall short)
   Multi-step reasoning / actions	Agent with tool calling
   Tabular prediction	Classical ML (XGBoost, sklearn) — not LLM

2. Model selection criteria:

   • Latency budget: streaming tokens arrive in ~200 ms for frontier models; embeddings are synchronous.
   • Cost: estimate (input_tokens × price_in + output_tokens × price_out) × daily_requests.
   • Privacy: does the data contain PII or regulated content? Use on-premises / private deployment (Ollama, vLLM, AWS Bedrock VPC endpoint).
   • Context window: choose a model whose context fits your largest expected document + prompt + output with 20% headroom.
   • Never hardcode a model name in application code — read from config/env so models can be swapped without deploys.

3. Prompt engineering:

   • Write prompts in version-controlled files (.claude/prompts/<name>.md or prompts/<name>.txt), not inline strings.
   • System prompt defines role, constraints, output format, and tone. Keep it ≤500 tokens.
   • Use few-shot examples when zero-shot produces inconsistent structure.
   • Separate instructions from data with clear delimiters (<document>, <user_query>, XML tags or ---).
   • Never interpolate raw user input directly — always sanitize or wrap in a delimiter.

4. RAG pipeline (when needed): a. Chunking: split documents into ~400–800 token chunks with 10–15% overlap. Chunk at semantic boundaries (paragraphs, sections), not fixed byte offsets. b. Embedding: embed chunks with the same model you'll use at query time. Store (chunk_id, text, embedding, metadata). c. Vector store: Pinecone, Qdrant, Weaviate, pgvector, or ChromaDB. Index chunk_id and filterable metadata fields. d. Retrieval: top-k cosine similarity (k=5–10) + optional metadata filter. Re-rank results with a cross-encoder if precision matters. e. Prompt assembly: system_prompt + retrieved_chunks + user_query. Include source citations in the context. f. Citation: instruct the model to cite [source_id] inline; validate citations point to real retrieved chunks. g. Freshness: re-embed and re-index documents when source content changes; track last_indexed_at.

5. Structured output:

   • Use JSON mode or function/tool calling — never parse free-text JSON from model output with json.loads directly.
   • Define output schema with Pydantic (Python) or Zod (TypeScript); validate before using in application logic.
   • Set temperature=0 for deterministic structured outputs; use temperature=0.7 only for creative generation.

6. Agents & tool calling:

   • Define each tool with a clear name, description, and typed parameters (the model reads these descriptions).
   • Tools must be idempotent where possible; flag destructive tools explicitly and require human confirmation.
   • Set a max iteration limit (e.g., 10 steps) to prevent runaway loops.
   • Log every tool call and its result for auditability.
   • Apply the principle of least privilege: agent tools can only access data/actions the user is authorized for.

7. Evals — build before shipping any AI feature:

   • Collect a golden dataset: 50–200 representative inputs with expected outputs / quality criteria.
   • Run evals on every prompt change and model upgrade.
   • Eval types: exact match, semantic similarity (cosine ≥ 0.85), LLM-as-judge (GPT-4 / Claude scores 1–5), human spot-check.
   • Track eval results in MLflow, Weights & Biases, or a simple CSV in Git.
   • Define minimum passing threshold before merging a prompt or model change.

8. Guardrails & safety:

   • Input filtering: detect and block prompt injection, jailbreaks, PII in prompts going to third-party APIs.
   • Output filtering: scan responses for PII leakage, harmful content, and hallucinated URLs before returning to users.
   • Use dedicated moderation APIs (OpenAI Moderation, AWS Comprehend) for user-generated content.
   • For regulated domains (medical, legal, financial): add a disclaimer and route edge cases to human review.
   • Rate-limit per user to prevent abuse and runaway cost.

9. Observability:

   • Log every LLM call: model, prompt_tokens, completion_tokens, latency_ms, cost_usd, request_id.
   • Trace multi-step agent runs end-to-end (LangSmith, Langfuse, Arize Phoenix, or OpenTelemetry).
   • Alert when: error rate > 1%, p95 latency > 5 s, daily cost > budget threshold.
   • Store a sample of inputs + outputs for ongoing quality review (with user consent / data policy compliance).

10. Production model serving:

    • Self-hosted: vLLM or TGI behind a load balancer; autoscale on GPU utilization.
    • Managed API: use retries with exponential backoff; handle rate-limit (429) and timeout (504) errors.
    • Caching: cache identical prompts (exact-match or semantic cache with Redis) to cut cost and latency.
    • Model versioning: pin to a specific model version in production; test new versions in staging with eval suite before promoting.

Standards

Do:

• Version-control every prompt; treat prompt changes as code changes (PR, review, eval).
• Always set a max_tokens / max_completion_tokens limit to prevent runaway output costs.
• Chunk and embed asynchronously; never block a request while indexing new documents.
• Separate retrieval logic from generation logic — testable independently.
• Use streaming responses for user-facing chat to reduce perceived latency.

Do not:

• Use an LLM for tasks a deterministic function can handle (arithmetic, date parsing, regex extraction).
• Embed user PII in vector stores without encryption and a documented retention/deletion policy.
• Deploy a new model or prompt to 100% of traffic immediately — use feature flags or canary rollout.
• Trust model output as ground truth without validation — always sanity-check structured outputs.
• Store API keys in source code or .env files committed to Git.

Common mistakes to avoid

• Prompt injection via user input: user supplies "Ignore previous instructions and...". Always wrap untrusted input in delimiters; never concatenate raw user text into the system prompt.
• No eval before ship: first version "feels good" in manual testing, but degrades on edge cases. Build the golden dataset on day one.
• Context window overflow: long documents silently get truncated. Measure token counts before every LLM call; truncate or chunk proactively.
• Hallucinated citations in RAG: model cites a source not in the retrieved chunks. Validate every citation against the retrieved set before returning to the user.
• Model name hardcoded: gpt-4 pinned in 30 files; model gets deprecated. Use a single config constant or env var.
• No cost controls: agent loops calling GPT-4 for 50 iterations per request. Set max_iterations, token budgets, and daily spend alerts.
• Skipping temperature=0 for structured output: non-zero temperature on JSON output causes intermittent parse failures in production.

Output format

A production AI feature module:

ai/
  prompts/
    <feature>_system.md
    <feature>_user.md
  rag/
    chunker.py
    embedder.py
    retriever.py
    indexer.py
  agents/
    <agent_name>/
      agent.py
      tools/
        <tool>.py
  evals/
    datasets/
      <feature>_golden.jsonl
    run_evals.py
    results/          # gitignored; tracked in MLflow/W&B
  serving/
    inference.py
    cache.py
  guardrails/
    input_filter.py
    output_filter.py
config/
  ai.yaml             # model names, thresholds, cost limits

Reference architecture template: .claude/templates/architecture.md

Related checklists

• .claude/checklists/security.md — prompt injection, PII, API key hygiene
• .claude/checklists/performance.md — latency, caching, token budgets
• .claude/checklists/launch.md — eval pass rate, guardrails, cost controls

Related agents

• .claude/agents/stack/ai/ — AI/ML stack selection and configuration
• .claude/agents/core/orchestrator.md — overall project flow
• .claude/agents/quality/ — testing strategy including LLM evals
• .claude/agents/engineering/ — feature implementation workflow