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Evaluation Guide 6 min read

How to Evaluate a Candidate's RAG Architecture Skills

RAG architecture has many moving parts: chunking, embedding, retrieval, generation. Here's how to evaluate a candidate's design and implementation skills.

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RAG Has Many Moving Parts Retrieval Architecture Generation Architecture Evaluation Framework Production Considerations Assess Full-Stack RAG Skills

RAG is more than calling LangChain with default settings. A real RAG architect makes dozens of decisions that affect accuracy, latency, and cost. Here's how to evaluate candidates on full-stack RAG architecture.

Retrieval Architecture

Legal: semantic boundaries (paragraphs, sections). Chat logs: by conversation turn. Code: by function or class. Discuss trade-offs of chunk size.

Vector search (semantic). Keyword search (exact terms). Hybrid (both). Re-ranking (post-process). Multi-query (multiple searches combined).

Generation Architecture

System prompt emphasizing 'only answer from context'. Few-shot examples. Format constraints (JSON, XML). Include 'context not found' fallback.

Smaller: cost-sensitive, high volume, simpler domain. GPT-4: complex reasoning, when accuracy matters most, lower volume.

Evaluation Framework

Retrieval: recall@k, MRR, NDCG. Generation: answer correctness, faithfulness, answer relevance. End-to-end: user feedback, A/B tests.

LLM generates answer not supported by retrieved context. Detect with LLM-as-judge, NLI models, or checking if answer contradicts context.

What Strong RAG Architects Explain Naturally

✦ Chunking trade-offs
✦ Embedding model selection criteria
✦ Hybrid search architecture
✦ Caching strategies
✦ Multi-tenant data isolation
✦ Cost versus accuracy trade-offs

Production Considerations

Re-index updated documents. Version vector DB. Handle deletions. Real-time vs batch updates - trade-offs.

Smaller embedding model. Faster vector DB (HNSW indexing). Cache frequent queries. Use smaller LLM. Parallelize retrieval and generation.

Candidate Evaluation Rubric

Retrieval

Strong Candidate: Can explain multiple retrieval approaches

Generation

Strong Candidate: Designs grounded prompts

Evaluation

Strong Candidate: Uses measurable metrics

Operations

Strong Candidate: Discusses monitoring and updates

Scaling

Strong Candidate: Understands latency and cost optimization

Assess Full-Stack RAG Skills

RAG architecture requires retrieval, generation, evaluation, and production skills. Offline Pixel pre-vets RAG engineers on all these dimensions. Raise a request, talk to candidates, fund the project, and approve payment when the work is done.

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