Source Ranking Issues

The Problem

Relevant documents ranked too low in retrieval results, causing less relevant or outdated sources to appear first and influence the AI response.

Symptoms

• ❌ Best answer in position 15, not top 3

• ❌ Generic content outranks specific

• �tml Language Models over GPT-4) → Domain-adapted: Continues training on your docs→ Better understands your terminology → Returns your specific results first

Cost: → Fine-tuning: $1000-5000 → Ongoing: Hosting custom model → Worth it for large-scale or specialized domains

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## How to Solve

**Implement reranking (Cohere Rerank, cross-encoder) after initial retrieval + boost recent documents with recency scoring + assign source authority weights + use domain-adapted embeddings or fine-tuning + increase K at retrieval, then rerank top-20 to top-5 + test ranking quality with NDCG@K metric + adjust boost weights empirically (not just guessing).** See [Ranking Optimization](../accuracy/source-priority.md).