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Beyond Basics: Advanced Techniques to Supercharge RAG Performance

Taylor Ye

• 5 min read
Bitdeer AI graphic: “Supercharge RAG Performance” showcasing architecture, retrieval, and reasoning with a glowing RAG stack.

In our previous blog, we introduced the fundamentals of Retrieval-Augmented Generation (RAG)—a hybrid architecture that combines the strengths of large language models (LLMs) with retrieval mechanisms to enhance factual accuracy and ground responses in external knowledge. While the basic RAG framework opens the door to powerful AI applications, production-grade deployments demand more: faster response times, better relevance, improved contextual understanding, and higher factuality.

In this post, we’ll dive deeper into how to increase the performance of RAG systems through architectural refinements, retrieval enhancements, fine-tuning techniques, and the emerging paradigm of Agentic RAG.

Quick Recap: What is RAG?

Retrieval-Augmented Generation consists of two major components:

  1. Retriever: Fetches top-k documents from a knowledge source using dense or sparse retrieval.
  2. Generator: A language model (like GPT, LLaMA) that conditions its response on the retrieved documents.

The benefit: LLMs can stay smaller and cheaper while maintaining up-to-date knowledge, and outputs are more interpretable because they're tied to cited evidence.

Improve the Retriever: Precision Starts Here

The quality of retrieved passages directly impacts the quality of generated responses. A performant retriever is non-negotiable.

a. Use Hybrid Retrieval (Dense + Sparse)

  • Dense retrieval (e.g., DPR, ColBERT, or E5) excels at capturing semantic similarity.
  • Sparse retrieval (e.g., BM25 or SPLADE) is good at exact match and rare word precision.
  • Hybrid models like ColBERTv2+BM25 or MultiRetriever pipelines can significantly increase recall and precision.

b. Train with Hard Negatives

Most retrieval models are trained with random negatives, which can result in overfitting to easy examples.

  • Use hard negatives: documents that are semantically close to the query but incorrect.
  • This forces the retriever to distinguish between subtle semantic nuances.

c. Retrieval Reranking

Introduce a reranker model (e.g., cross-encoder like MonoT5 or BGE-Reranker) to rescore the top-k retrieved documents. Though slower, reranking improves relevance significantly.

  • Tradeoff: May increase latency, so consider batching or caching reranker results.

d. Query Rewriting and Expansion

Queries from users are often ambiguous. Use techniques like:

  • T5-based query rewriting
  • Pseudo-relevance feedback (expanding queries using top-k terms from initial retrieval)
  • Context-aware query expansion in multi-turn dialogues.

Optimize the Knowledge Corpus

A performant RAG depends on a clean, relevant, and well-indexed knowledge base.

a. Preprocessing and Chunking

  • Chunk documents intelligently using sentence boundary detection or semantic segmentation.
  • Avoid fixed token windows that can split ideas mid-thought.

b. Metadata Filtering

Attach metadata (e.g., timestamp, author, document type) and use it for pre-filtering before dense retrieval. This allows better targeting and lower embedding search cost.

c. Embedding Refresh Policy

Recompute embeddings periodically to reflect improvements in retrievers or model embeddings.

  • For example, moving from E5-small to E5-large embeddings may yield better contextual matches.

Generator-Level Enhancements: Grounded and Controlled Outputs

The generation phase is where hallucinations can occur. Here's how to rein them in.

a. Fusion-in-Decoder (FiD) Models

Instead of concatenating documents, pass each retrieved doc as a separate encoder input and let the decoder attend to all of them.

  • This prevents positional bias and improves document grounding.
  • Use FiD-based architectures like T5-FiD, FLAN-T5-FiD.

b. Retriever-Generator Joint Fine-tuning

End-to-end fine-tuning aligns both retriever and generator to the same objective: maximizing answer accuracy.

  • Loss: Cross-entropy loss on generated tokens + contrastive loss for retriever.
  • Risk: Requires GPU-intensive compute and large-scale QA datasets (e.g., Natural Questions, HotpotQA).

c. Context-Aware Generation Prompts

Embed retrieved docs in well-structured prompts that explicitly instruct the model to cite or synthesize.

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Can also add confidence estimation prompts to allow the model to abstain from answering if evidence is insufficient.

Memory, Caching, and Latency Optimizations

For real-time systems, performance isn't just about relevance, it's also about speed.

a. Vector Caching

  • Cache common queries and their top-k vector search results to avoid redundant computations.
  • Use semantic hashing or approximate query deduplication.

b. Asynchronous Retrieval + Generation

  • Run retrieval and generation asynchronously using non-blocking IO or multi-threaded architecture.
  • Pre-warm retrieval results during user typing (“search-as-you-type” pattern).

c. In-Memory Indexing

  • Use in-memory FAISS or Milvus for high-performance environments where latency matters.
  • Alternatively, use quantized or HNSW-based indexes for sub-ms retrieval.

Introducing Agentic RAG: From Retrieval to Reasoning

Traditional RAG works well for static QA. But what if the user query requires multiple steps of reasoning, tool use, or decision-making? That’s where Agentic RAG comes in.

What is Agentic RAG?

Agentic RAG combines RAG with autonomous agent frameworks that break down complex tasks into subtasks, each involving iterative retrieval, planning, and tool invocation.

Key Features:

  • Planner-Executor Loop: An LLM agent plans the steps (e.g., "search definition", "gather opinions", "compare methods") and uses RAG to fetch context.
  • Multi-hop Retrieval: Each step has its own retrieval query, informed by previous outputs.
  • Tool Use: Agents can call APIs (e.g., calculator, code interpreter) alongside RAG.
  • Memory and State Tracking: Agents maintain dialogue or task state across turns.

Example Use Case:

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Agentic RAG Flow:

  1. Plan: Identify key tax differences between LLC and S-Corp.
  2. Retrieve: Query IRS website and CA-specific laws.
  3. Generate: Produce intermediate summaries.
  4. Compare: Use table-based generation to synthesize.
  5. Respond with citations and disclaimers.

Tools and Frameworks:

  • LangChain Agents or AutoGen for orchestrating agent workflows.
  • Use tool calling models like Llama 3.2 with ReAct, or AutoGPT-style planning loops to implement reasoning + retrieval hybrids. 

Evaluation: Precision, Recall, and Factuality

High performance means nothing without measurement.

Key Metrics:

  • Hit@k: Measures if the correct document is in the top-k retrieved.
  • Answer Exact Match (EM) and F1: Compare generated answers to gold references.
  • Faithfulness Score: Measures how grounded the generation is in the retrieved evidence.
  • Latency & Throughput: Especially important in production RAG systems.

Evaluation Tools:

  • RAGAS: RAG-specific evaluation framework for factuality and relevance.
  • LLM-as-a-Judge: Using GPT to score factuality against the context.
  • BEIR Benchmark: Standard retrieval benchmarks (e.g., TREC, FiQA, SciFact).

Future Direction: Towards Composable and Modular RAG

We're seeing a shift from monolithic RAG systems to composable AI pipelines:

  • Modular retrievers + LLMs + agent planners.
  • Plug-and-play embedding models, rerankers, and vector DBs.
  • Standardized interfaces via LangChain, LlamaIndex, or DSPy.

The next-generation RAG systems will be:

  • Adaptive: Dynamically adjust the number of docs or reasoning depth.
  • Contextual: Aware of user profile, history, and prior interactions.
  • Multimodal: Fuse retrieval from text, tables, and images (e.g., RAG-VL for vision-language tasks).

Final Thoughts

Performance tuning for RAG is a multi-dimensional challenge spanning retrieval science, generative modeling, prompt engineering, and now autonomous agent design. With Agentic RAG, we’re moving into a new era where retrieval is not just about context injection, but an integral part of reasoning and planning workflows.

Whether you're building customer support bots, enterprise search assistants, or research copilots, the path to robust, scalable, and intelligent RAG starts with careful engineering at every layer, from the retriever to the reasoning loop.