The Context Problem in Enterprise AI
When enterprises explore large language models, the first instinct is often to chase the newest, largest model. "GPT-5 will solve our accuracy issues," or "We need Claude Opus for this." But after working with dozens of organizations deploying RAG systems in production, we've learned that model size is rarely the bottleneck.
The real challenge is context: getting the right information to the model at the right time, with the right permissions, in a format it can use reliably. A smaller model with excellent retrieval will outperform a massive model searching through irrelevant documents every single time.
Why Retrieval Matters More Than Model Size
Think about how you'd answer a complex question at work. You don't try to remember everything — you look up the latest version of a policy, check your email for context, or ask a colleague who worked on that project. RAG systems should work the same way.
The quality of your answers depends on three things: finding the right documents, checking that the user has permission to see them, and presenting them in a way the model can synthesize effectively. None of these improve by switching from a 70B to a 405B parameter model.
We've seen clients improve accuracy from 60% to 92% by refining their chunking strategy, adding metadata filters, and implementing hybrid search. The model stayed exactly the same. The context got better.
Building Reliable Retrieval Systems
Start with the task. What decision will this answer support? If it's a compliance question, you need citations and version tracking. If it's a customer support query, you need freshness and fallback to human escalation. The retrieval architecture should match the risk profile.
Access control is non-negotiable. Your RAG system should never surface a document the user couldn't access directly. This means checking permissions at query time, not just at indexing time. It also means logging what was retrieved and why, especially for regulated industries.
Evaluation comes next. Before you roll out to 10,000 users, test on 100 real queries with known-good answers. Measure precision and recall on your retrieval. Measure accuracy on the final generated answer. Track latency and cost per query. Fix what's broken before it scales.
When to Use Bigger Models
Larger models do help in specific scenarios: complex reasoning tasks, multi-step synthesis, or when you need the model to follow nuanced instructions. But even then, the context you provide matters more than the model's parameter count.
For most enterprise search use cases — policy lookup, document Q&A, knowledge base search — a well-tuned retrieval system with a mid-size model (7B-70B parameters) will deliver better results at lower cost than a massive model with poor retrieval.
Practical Next Steps
If you're building or improving a RAG system, start here: audit your retrieval quality. Are you finding the right documents? Are you respecting permissions? Are you providing enough context without overwhelming the model? These questions matter more than which model you choose.
Build an evaluation set of 50-100 real queries with expected answers. Measure your system against it. Iterate on retrieval, chunking, and prompt design. Only after you've maxed out those improvements should you consider a larger model.
Context beats scale. Every time.