Case-Study: How We Cut RAG Latency by 40% with Systematic Testing

Retrieval-Augmented Generation (RAG) is the backbone of modern, context-aware AI applications. It's the magic that allows AI agents to answer questions using your private data, providing relevant, factual responses. But there's a common, frustrating problem: RAG pipelines can be slow. High latency not only degrades the user experience but can also make a service feel unreliable and expensive to run.

We faced this exact challenge with one of our core AI services. Our initial RAG pipeline provided accurate answers, but its response time was lagging, averaging a sluggish 2 seconds per query. In the world of real-time interaction, that's an eternity.

Instead of guessing and making random tweaks, we adopted a methodology of systematic testing. This case study breaks down how we used Experiments.do to run structured A/B tests, validate our changes, and ultimately cut our RAG latency by 40% while also improving relevance and reducing cost.

The Challenge: A "Good Enough" Pipeline Isn't Good Enough for Production

Our initial pipeline, let's call it rag-v1_baseline, looked solid on paper. It used a powerful LLM to ensure high-quality synthesis and a standard vector database for retrieval.

Baseline Performance:

• Relevance: 0.90 (Good, but not perfect)
• Latency: ~2000ms (Too slow)
• Cost: Moderately high due to the powerful model

The latency was our biggest pain point. We knew we had to improve it before shipping, but we couldn't afford to sacrifice the quality of the responses. How could we make it faster and cheaper without making it dumber? This is a classic dilemma in LLM Validation and requires a structured approach.

The Approach: Systematic AI Experimentation

Instead of changing multiple variables at once, we broke the problem down. We used Experiments.do to design and run a series of isolated A/B tests on the most critical components of our agentic workflow.

Our experimentation plan focused on two key areas:

1. The Retriever: How efficiently could we find the right context? We hypothesized that our chunking strategy and choice of vector database were major contributors to latency.
2. The Generator (LLM): Was our large, powerful model overkill? We wanted to test if a smaller, faster model could achieve similar or better relevance scores when paired with an optimized prompt and better context.

For each experiment, we tracked three core metrics:

• relevance_score: Assessed by an evaluation LLM for quality and correctness.
• latency_ms_avg: The average end-to-end response time.
• cost_per_query: The operational cost for each run.

This process of RAG Evaluation is critical. Intuition can be misleading; only hard data can reveal the true winner.

The Results: Data-Driven Optimization Wins

Our first few experiments yielded incremental gains. We tested different chunking methods and found a configuration that improved retrieval speed by about 15%. Then, we A/B tested our baseline LLM against a faster, more cost-effective model.

The real breakthrough came when we combined these wins into a new candidate, rag-v2-optimized, and ran it against our original rag-v1_baseline in a final, decisive experiment.

Experiments.do made it simple to configure this test and automatically collect the results. The platform gave us a clear, unambiguous answer.

Here’s a snapshot of the final experiment's results, managed through the platform:

{
  "experimentId": "exp-final-rag-test",
  "name": "Final RAG Pipeline Optimization",
  "status": "completed",
  "winner": "rag-v2-optimized",
  "results": [
    {
      "variantId": "rag-v1_baseline",
      "metrics": {
        "relevance_score": 0.90,
        "latency_ms_avg": 2000,
        "cost_per_query": 0.0030
      }
    },
    {
      "variantId": "rag-v2-optimized",
      "metrics": {
        "relevance_score": 0.92,
        "latency_ms_avg": 1200,
        "cost_per_query": 0.0018
      }
    }
  ]
}

The data speaks for itself. The winning variant, rag-v2-optimized, was not just a little better—it was a game-changer:

• Latency dropped from 2000ms to 1200ms, a 40% reduction.
• Relevance score increased from 0.90 to 0.92. The faster model, fed with better context from our optimized retriever, performed more accurately.
• Cost per query was cut by 40%, from $0.0030 to $0.0018.

This is the power of systematic AI experimentation. We didn't have to guess or hope. The data showed us the definitive path to a superior product. With this confidence, we promoted the winning variant to production.

Key Takeaways for Building High-Performance AI

1. Don't Guess, Test: Your intuition about which model or prompt is "best" is often wrong. Structured A/B testing is the only way to know for sure.
2. Isolate Variables: Test one component at a time—your retriever, your reranker, your LLM, your prompt. This helps you pinpoint exactly what works.
3. Define Success Metrics: You can't improve what you can't measure. Define clear metrics like latency, cost, and relevance before you start.
4. Embrace Continuous Improvement: This isn't a one-time fix. We integrated Experiments.do into our CI/CD pipeline to continuously test and validate new models and configurations, ensuring our AI services are always running at peak performance.

Validate and Optimize Your AI Agents Today

Building robust, production-ready AI is a science. By moving from guesswork to a data-driven validation process, we were able to achieve significant performance gains and ship our AI service with total confidence.

Ready to stop guessing and start optimizing? Experiments.do provides the framework you need to run comprehensive experiments on prompts, models, and RAG pipelines.

Sign up for free at Experiments.do and find your highest-performing configurations.