Context windows are the invisible ceiling in every production LLM application. You can have the best model, the cleanest data pipeline, and a product people love - but if you can't manage conversation history efficiently, you hit a wall. Costs spiral, latency climbs, and response quality degrades.
A travel platform engineering team reduced tokens per request by 64% while improving both response speed and conversation quality. Their approach is worth studying because it solves a problem every builder shipping conversational AI will face.
The Core Problem
LLM context windows have limits. GPT-4 supports up to 128,000 tokens, but in practice, you don't want to use all of it. Longer contexts mean slower responses and higher costs. More importantly, cramming everything into the window doesn't guarantee better outputs - it often degrades them. The model gets lost in irrelevant detail.
The naive approach is to truncate old messages when you hit the limit. That works until a user references something from earlier in the conversation and the model has no idea what they're talking about. Conversations break. Users notice. Trust erodes.
The challenge is keeping conversations coherent across long sessions without paying for tokens you don't need. That requires deciding what to keep, what to compress, and what to discard - without breaking the continuity that makes conversations useful.
The Strategy That Worked
The team implemented four complementary techniques, each handling a different aspect of the problem.
Sliding window with summarisation. Instead of dropping old messages entirely, they summarise them. The model sees recent exchanges in full detail, plus compressed summaries of earlier conversation. A 50-message thread might be represented as 10 recent messages plus a 200-token summary of what came before. The user can reference earlier topics and the model has enough context to respond coherently.
Relevance-based retrieval. Not all conversation history is equally relevant to the current query. If a user asks "What hotels did we discuss yesterday?", the model needs those specific messages - but not the unrelated tangent about flight times. The system uses semantic search to retrieve the most relevant prior messages for each query, keeping the context window focused on what actually matters.
Structured memory for critical facts. Some information needs to persist across the entire conversation without being summarised or compressed. User preferences, booking constraints, confirmed decisions - these go into a structured memory layer that's always available to the model. A simple key-value store, formatted for easy parsing. This prevents critical details from being lost in summarisation.
Context compression. The final layer is aggressive pruning of verbose outputs. Travel queries often generate long lists - hotel options, flight times, amenity details. The system compresses these into dense, structured formats that preserve information while slashing token count. A 500-token hotel comparison becomes a 100-token table with the same decision-relevant data.
The Numbers
The result was a 64% reduction in tokens per request. For a platform handling thousands of conversations daily, that translates to significant cost savings. More importantly, response latency improved - fewer tokens means faster processing. And conversation quality didn't suffer; in user testing, satisfaction scores actually increased.
The counterintuitive finding: less context often produces better responses. When the model sees only relevant history instead of everything, it focuses on what matters. The quality improvement wasn't despite the compression - it was because of it.
What This Means for Builders
If you're building conversational AI, context management isn't optional infrastructure you add later. It's a first-class design problem. The approaches here work because they're tailored to the specific use case - travel booking has clear structure, defined decision points, and natural conversation boundaries. Your domain will have different patterns.
The principles translate across domains: identify what's critical and keep it, compress what's useful but bulky, retrieve what's relevant on demand, and discard what doesn't contribute to the current query. The implementation details will differ, but the strategy is sound.
The trap to avoid is treating the context window as a constraint you work around begrudgingly. It's actually a forcing function for better design. When you're forced to decide what matters in a conversation, you build systems that focus on signal rather than preserving noise. The constraint makes the product better.
Production LLM applications are moving past the proof-of-concept phase where throwing tokens at a problem was viable. The platforms that scale are the ones that treat token budgets as a design parameter, not an operational cost to optimise later. This travel platform got there by building context management into the architecture from the start. That decision is showing up in their unit economics now.