10 techniques to improve RAG accuracy

Retrieval-augmented generation (RAG) has rapidly become the go-to pattern for combining large language models (LLMs) with up-to-date, domain-specific knowledge. By grounding generative output in retrieved context, RAG dramatically reduces hallucinations and boosts factuality—and yet, dialing in peak accuracy remains a moving target. For more details, see our RAG getting started guide.

In practice, teams wrestle with how to chunk documents, when to layer in sparse versus dense retrieval, and—above all—how to measure “what’s better” in a principled, reproducible way.

At Redis, we’ve guided scores of enterprises through this maze. Our playbook is simple:

1. Baseline and metrics: Spin up a naïve RAG pipeline—vector-only retrieval, off-the-shelf LLM—ingest a small sample of your corpus.
2. Define quantitative metrics (e.g., retrieval recall, answer F1, human preference). We suggest using frameworks like RAGAS for this.
3. Iterate and hill-climb: Use real-world feedback to pinpoint weakest links, then methodically apply and measure targeted improvements.

Below, we enumerate 10 techniques that consistently deliver the largest accuracy gains—and explain exactly when (and why) to apply each one—while spotlighting how Redis’ AI stack, including Redis Query Engine (our vector and hybrid search capability), makes experimentation and production at scale a breeze.

1. Hybrid search

Combining exact keyword matching with semantic vector search ensures that both precise terms and conceptually related content are retrieved. This dual-layered approach is especially useful in mixed or structured corpora—for instance, legal documents or technical manuals—where important keywords (e.g., part numbers, regulatory codes) may not be semantically similar to a user’s query.

Hybrid search significantly boosts recall and contextual quality by addressing lexical gaps, particularly in environments where domain-specific jargon or synonyms matter. Redis supports hybrid queries natively through Redis Query Engine, letting you experiment with combinations of BM25 and vector similarity from a single query interface. For more details see our tutorial on hybrid search using Redis.

This technique is especially useful when your corpus contains a mixture of structured and unstructured data, or when precise terminology is essential to the query. By bridging lexical and semantic retrieval, hybrid search helps avoid missing critical documents that rely on exact phrasing, while still capturing related concepts.

2. Tuning HNSW indices

A subtle but powerful technique to improve RAG accuracy lies in tuning the internal structure of your vector index—specifically, by developing denser HNSW (Hierarchical Navigable Small World) search graphs. Denser graphs reduce missed connections during search traversal, improving both recall and retrieval consistency, especially when your corpus contains similar but subtly distinct entries (e.g., FAQ variants, similar product specs). By tuning parameters like `M` (neighbor count) and `efConstruction` (search depth), you can balance accuracy with performance.

Redis keeps these dense graphs in memory, making it possible to scale precision without incurring latency penalties. This approach is particularly valuable in production deployments where consistency under load is critical. Since Redis Query Engine is the fastest benchmarked vector database, you can easily trade-off that extra performance for denser graphs that will deliver higher precision. In other words, Redis Query Engine can deliver better accuracy for vector search at the same latency as other vector databases.

This technique shines when your corpus has many near-duplicate or closely related documents, as it helps avoid missed retrievals that can confuse the LLM and degrade output quality. By improving the connectedness of the search graph, it ensures more robust and repeatable retrieval results.

3. Chunking & parsing optimization

Optimizing how you chunk your documents—by sentence, paragraph, or semantically coherent sections—can dramatically change what context gets retrieved. For example, medical records or policy manuals benefit from structured parsing that respects logical boundaries; random or overly long splits risk diluting context relevance. Semantic-aware chunking preserves topic coherence and maximizes the chance of fully answering a user’s query in fewer hops. Redis integrates cleanly with chunking-aware frameworks like LangChain (see here for an example) and LlamaIndex, enabling easy iteration across formats.

This is most useful when your documents have complex structure or when retrieval granularity significantly affects answer quality. Proper chunking helps the retriever surface relevant, concise passages that are well-aligned with user questions.

4. Fine-tune your embeddings

Generic embedding models often underperform in specialized domains like law, finance, or pharmaceuticals. Fine-tuning embeddings using contrastive learning or domain-specific corpora helps capture subtle term relationships and context-specific meanings. This is especially valuable when standard vector search misses relevant content because semantic distances don’t align with domain intuition. Redis supports storing and querying multiple embedding models in parallel, making it easy to test and transition between generic and fine-tuned versions without redeploying your stack. For a deeper look, check our blog on Fine-tuning embeddings for RAG.

Use embedding fine-tuning when your domain uses nuanced language or terms with very specific meanings that generic models fail to capture. This technique improves vector retrieval precision by aligning embeddings closer to domain semantics.

5. Fine-tune the LLM

If your RAG system frequently fumbles response tone, format, or specific instructions (like citation formats or safety disclaimers), LLM fine-tuning can be a game-changer. It’s especially effective in high-stakes domains like healthcare or finance, where generic completions risk legal or ethical issues. Fine-tuning helps the model internalize task-specific structure and content expectations, reducing over-reliance on prompt engineering. Redis enables efficient context delivery to these fine-tuned models, ensuring low-latency execution even as models become more specialized.

This technique fits best when your use case requires strict adherence to domain-specific response styles or regulatory constraints. It helps reduce errors and inconsistencies in generated text by embedding specialized knowledge directly in the LLM.

6. Semantic caching

For use cases with a stable knowledge base—like FAQs, product documentation, or internal policy queries—using semantic caching in persistent mode allows you to preload high-confidence answers and serve them reliably. This dramatically improves RAG accuracy by reducing reliance on the variability of LLM-generated completions, especially when questions have well-defined, repeatable answers. By matching user queries to this cached FAQ set using vector similarity, you ensure that frequently asked questions are answered deterministically and with minimal latency. Redis’ LangCache enables persistent, semantic-aware lookup of pre-approved answers—essentially turning your FAQ into a high-precision, zero-shot knowledge store. This makes it easier to deliver consistent, trusted responses even under heavy load. See our LangCache docs here.

7. Long-term memory management

When your app involves multi-turn dialogue or long-running interactions—such as coaching apps, internal copilots, or support agents—managing long-term memory becomes essential. By persisting user interactions or key facts across sessions, the system avoids context reset and maintains coherence. Redis’ native vector storage, fast lookups, and time-series support allow for the implementation of short- and long-term memory buffers, improving personalization and follow-up accuracy over time. More on this in our blog Memory management for AI agents. Redis also integrates with frameworks such as LangGraph to provide an out-of-the-box experience for short term memory and long-term memory.

This approach is critical when the system needs to maintain user or context state over extended periods, ensuring continuity and reducing repetitive questioning or context loss.

8. Query transforms

When users submit vague or under-specified queries, techniques like HyDE (Hypothetical Document Embeddings) or multi-step reformulations can fill in the gaps by enriching the original query with inferred context. These methods are particularly useful in open-domain systems, knowledge bases, or any interface where queries come in terse or ambiguous. Query transforms increase the chance of retrieving meaningful context before LLM generation begins.

Apply query transforms when your input data is noisy, incomplete, or informal. They enhance retrieval by expanding or clarifying intent, helping to pull better context for the LLM.

9. LLM as judge

Using an LLM as a judge—evaluating the faithfulness of generated responses against the retrieved context—can significantly sharpen RAG system accuracy. Instead of relying on coarse heuristics or manual reviews, the LLM acts as a second-stage evaluator, scoring whether the answer truly reflects the source material. This is especially powerful in cases where hallucinations are costly, such as in legal, healthcare, or enterprise search apps. Frameworks like RAGAS operationalize this approach, enabling fast, scalable measurements of factual consistency and helpfulness. Redis integrates seamlessly with these evaluation loops and observability pipelines, allowing teams to test retrieval strategies, track fidelity over time, and automate A/B comparisons.

10. Re-ranking

When top-k retrieval brings back a mixed bag of useful and irrelevant documents, re-ranking helps reorder the results to prioritize helpful context. This is critical in noisy corpora or where embedding distances alone are insufficient. You can re-rank via shallow ML models or even with a secondary LLM pass. Fusion techniques—combining results from different retrievers—boost robustness and recall. Redis’ support for hybrid search and secondary scoring enables efficient implementation of these patterns at scale. Read our blog on how information retrieval can be improved with fine-tuned rerankers.

Use re-ranking when retrieval precision is low due to noise or when you have multiple complementary retrieval sources. These techniques improve result quality by refining and merging ranked outputs.

The path to more accurate RAG

While these ten methods form a robust toolkit for climbing the RAG-accuracy hill, advanced users may also explore prompt tuning, metadata conditioning, negative sampling, or agentic RAG orchestration. The key is always: start simple, measure rigorously, and evolve iteratively.

Learn more:

• Browse Redis’ full set of RAG and GenAI resources.
• Try Redis’ vector database firsthand with our free trial.
• Ready to take your RAG pipelines from “good” to “best”? Reach out to our team, and let’s build the next generation of reliable, high-accuracy AI apps—together.