Inference Engineering: Giving AI the Right Information

A research reference based on inference engineering for building systems that give AI the right information, at the right time, at the right cost. Read how caching, hybrid retrieval, progressive disclosure, model routing and a system of context around the model decide what an AI system can actually reason over.

Most enterprise AI conversations start with the choice of model.

The model is rarely what limits the system.

A model can only reason over what it can see at the moment it decides.

Give a strong model the wrong context and it produces a confident, wrong answer.

Give a smaller model the right context and it can outperform it.

Choosing the model is the easy part.

Deciding what it sees is the engineering.

Why This Matters

Context decisions carry costs that do not show up where they are made. A summary written to save tokens changes the start of the prompt, so the provider can no longer reuse the computation it already performed, and the smaller context can cost more than the one it replaced.

Search that works on meaning misses what engineering depends on. A query for a specific fault code returns documents about similar faults, when the one record that mattered was the exact identifier, and keyword search would have found it at once.

Long-running work loses the detail it needs. A requirement to tolerate exactly seventeen minutes of disconnected operation survives summarization as a requirement for temporary offline operation. The idea remains. The number does not.

And without instrumentation, none of it is visible. Cost per call looks acceptable while cost per successful outcome climbs, because nobody recorded which context produced which result.

This creates a structural exposure.

• Inference cost rises as context is rewritten rather than reused.
• Retrieval returns what is similar rather than what is exact.
• Critical constraints disappear in summaries and sliding windows.
• Stale tool output crowds out the evidence the model needs.
• Every task goes to the same model, whatever it demands.
• The organization's context lives wherever its tools happen to store it.

The model reasons. The system decides what it reasons over.

Choosing a better model does not fix an architecture that shows it the wrong information.

What You'll Learn

Inside the reference, you'll find:

• Why the limiting question in enterprise AI has moved from what to ask the model to what the model should know
• How prompt caching works, what a cache hit is, and why recomputation makes some token savings expensive
• How to order a prompt so the cache can be reused, and the five practices that raise the cache-hit ratio
• Why caching and memory solve different problems, and what happens when one is asked to do the other's job
• How retrieval-augmented generation, chunking and tools decide what a model can see
• When dense semantic search works, when BM25 keyword search works, and why hybrid search with reranking outperforms either alone
• Why retrieval is not reasoning, and how to keep the two layers separate
• How progressive disclosure and a navigable knowledge hierarchy let a model find detail without loading the library
• How model routing matches each task to the smallest model that can do it well
• What sliding windows, compression, selective retention and summarization each trade away
• What context rot is, and why stale tool output is often the largest source of it
• Which measures to log, and why cost per successful outcome matters more than cost per call
• How an enterprise ontology and agentic memory become sources of context the model reasons over
• Why the context, not the model, is the asset the organization should own
• How a system of context becomes a system that improves from its own outcomes

Download the Research Reference

Read the complete reference on inference engineering, covering the economics of context, retrieval architecture, long-running context management, instrumentation, and the system of context that turns an AI application into a system that improves with use.

Models will keep improving, and every organization will have access to them. The organizations that decide what those models see, and keep that context as their own, will be the ones whose systems get better with every decision.