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RAG and RLHF workflows

RAG and RLHF address two of Gen AI’s biggest risks: hallucination and misalignment. This page outlines how to structure both workflows using shared data foundations.

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Retrieval-Augmented Generation (RAG)

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Key challenges

  • Low-quality or outdated sources
  • Poor chunking strategies
  • Weak retrieval relevance
  • Silent failure modes
  1. Curate trusted knowledge sources
  2. Embed and cluster content
  3. Evaluate retrieval relevance
  4. Annotate failures and gaps
  5. Refine sources and prompts
Recommended docs

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RLHF and preference learning

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Common feedback signals

  • Binary correctness
  • Pairwise preference
  • Safety and policy compliance
  • Style and tone alignment
  1. Define feedback ontologies
  2. Collect structured human judgments
  3. Review for consistency
  4. Analyze trends and disagreement
  5. Iterate on data and prompts
Recommended docs

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Bringing RAG and RLHF together

The strongest Gen AI systems combine both:
  • RAG reduces hallucinations
  • RLHF improves behavior and alignment
  • Evaluation connects the two
Together, they form a continuous improvement loop.

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Key takeaway

Gen AI quality improves fastest when grounding and alignment are treated as first-class data problems, not post-hoc fixes.