How to Build a State-of-the-Art Search Stack for LLMs: RAG, Reranking, and Reinforcement Learning

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As AI systems increasingly rely on external context to generate accurate, helpful responses, high-performance search infrastructure is no longer optional - it's foundational. Whether you're building chatbots, developer tools, or enterprise AI agents, retrieval quality determines model output quality.

This article walks through the modern AI search stack, explains why reranking is essential, and introduces cutting-edge techniques for training retrieval models using reinforcement learning.

Why LLMs Need Better Search, Not Just Better Models

Large language models (LLMs) don’t “know” things in the traditional sense. They rely heavily on context (structured or unstructured input) to generate grounded responses. Yet most organizations have data scattered across PDFs, codebases, emails, and internal documents.

This is where retrieval-augmented generation (RAG) comes in. Despite recent skepticism, RAG isn’t obsolete. What has changed is the sophistication of how we retrieve and feed information into models. The focus is shifting toward context engineering—strategically deciding when and how to retrieve, rather than doing it once and hoping for the best.

Modern AI Agents Don’t Search Once. They Decide When to Search.

Embedding-based search alone isn’t enough. It can miss the mark when nuance matters. Newer architectures give the model agency: it can determine whether more information is needed and perform follow-up searches before generating a final answer. This reduces hallucinations, improves latency and token efficiency, and enhances result accuracy.

Anatomy of a High-Performance Search Stack

To build robust search into an AI system, you need a multi-stage approach:

1. First-Stage Retrieval: Broad and Fast

Goal: Surface as many potentially relevant documents as possible.

Common techniques include:

• BM25 (text-based ranking)
• Vector search (dense embeddings)
• Hybrid search (combines lexical and vector)
• Prefix or fuzzy matching

These methods are optimized for speed and recall, not accuracy.

2. Second-Stage Retrieval (Reranking): Precise and Expensive

Here’s where the magic happens. Once you have candidates, you apply a more compute-heavy reranker to score and reorder them based on actual relevance.

Tools for reranking:

• Cross-encoders
• LLMs fine-tuned for ranking
• Domain-specific heuristics
• Multimodal inputs (metadata, file types, source)

Reranking improves grounding, reduces model error, and is especially valuable in domains like code generation, tool usage, and technical support.

3. Postprocessing: Clean, Filter, and Optimize

Final tweaks often include:

• Deduplication
• Freshness ranking
• Metadata filtering
• Latency-aware scoring

Why Reranking is Critical (And Often Ignored)

In most real-world applications, first-stage search gets you close, but not close enough. Without reranking, even highly relevant results may be buried or out of order. Adding a reranker on top of your existing stack—such as ElasticSearch or a vector DB—can dramatically improve the quality of AI outputs.

Reranking also gives teams flexibility:

• Easier to fine-tune than embedding models (no reindexing needed)
• Faster to deploy with lightweight inference options
• Lower latency through distillation

Training Rerankers with Reinforcement Learning: A New Frontier

New reranking models like Mxbai-Rerank-Large-V2 showcase how reinforcement learning (RL) is being used to optimize retrieval pipelines. These models outperform static ranking systems by learning from both synthetic and real-world interaction data.

Key Training Insights:

• Use a diverse dataset: Mix synthetic prompts, real-world queries, and noisy user-generated content.
• Apply multi-stage supervision:
  • Stage 1: Label partial datasets using LLMs
  • Stage 2: Train with constructive ranking loss on the full dataset
  • Stage 3: Inject human preferences and production search logs for final tuning
• Support multilingual and multimodal content: Text, code, images, audio, and video

The result is a retrieval system that adapts better to downstream use cases, from dev tools to enterprise chat agents.

Quick Integration: Add Reranking to Your Stack

You don’t need to overhaul your system to benefit from reranking. Example (Python):

    
      from together import Together

      client = Together(api_key=TOGETHER_API_KEY)

      response = client.rerank.create(
          model="mixedbread-ai/Mxbai-Rerank-Large-V2",
          query=query,
          documents=documents,
          top_n=10
      )
    

A few lines of code can transform an average retrieval experience into a best-in-class pipeline.

What’s Next for AI Search?

The AI search ecosystem is still maturing. Key challenges include:

• Fragmentation across tools and formats
• Limited multimodal and multilingual support
• Complex pipelines with brittle components (chunking, embedding, indexing)

Next-generation platforms like multivector vector stores are solving these by offering frictionless pipelines built on models that natively handle text, code, images, and more.

Final Thoughts

Search isn’t just infrastructure - it’s a competitive edge. As LLMs become more context-aware, investing in reranking, fine-tuning, and intelligent retrieval systems will directly impact product quality, performance, and user trust.

Give your AI the right context, and it will do the rest.