Embedding AI Features Safely: Guardrails, RAG & Evaluation Loops

Embedding AI into core workflows requires balancing capability with controllability. We design AI features as pipelines with checkpoints, not black boxes.

Architecture Overview

1. User intent captured & normalized.
2. Retriever fetches contextual chunks (semantic + metadata filters).
3. Prompt assembly (system + guard + contextual + user).
4. LLM invocation with timeouts + retry strategy.
5. Validation (toxicity, PII, hallucination heuristics).
6. Fallback (template / deterministic rules) if validation fails.

Guardrails

• Max context window & chunk deduplication.
• Prompt linting (disallowed instructions, jailbreak patterns).
• Output classifiers (toxicity / leakage).
• Confidence scoring (retrieval coverage + answer overlap).

Retrieval Isolation

const context = retriever.retrieve(query, { k: 8 });
if (context.coverage < 0.4) return fallbackAnswer();

Continuous Evaluation

We maintain a golden set of anonymized prompts + expected attributes. Each model / prompt change runs offline eval scoring factuality, latency, cost.

Metrics

• Task success (user accepted suggestion).
• Fallback rate < target threshold.
• Latency p95 within UX budget.
• Safety violation count trending downward.

Structured pipelines + evaluation loops turn AI from novelty into dependable product leverage.