103 - Context, compression, and memory

The previous lesson covered tokens as a unit of consumption. This lesson covers the place they all live: the context window. It is the model's entire working memory for a single request, it has a ceiling, and every long session is a small balancing act between keeping that memory, compressing it, or clearing it and starting fresh.

The point of the lesson is not to turn you into a context engineer. Plexara and your client handle most of this automatically. The point is to recognize what the three plays are, so you know which one you are making.

A context window is the maximum number of tokens a model can read and write inside a single call. It is not separate buckets for prompt, history, tools, and response. It is one budget. Every layer of the conversation lives in the same budget and competes for the same space.

This matters because the window is shared. A long system prompt leaves less room for conversation history. A verbose tool result leaves less room for the model's reasoning. A detailed response leaves less room for whatever tool call follows it. The model treats all of it as one sequence of tokens.

Specific numbers change roughly quarterly as providers ship new tiers. As of April 2026 the confirmed top-end on Claude is Claude Opus 4.7 at one million tokens. The industry baseline on frontier tiers sits at 200,000 tokens, itself a hundred-fold increase over the original GPT-3 six years ago. The trend is real growth, but growth has slowed as provider focus has shifted to reasoning quality over raw capacity.

Practical rule: pick the right tier for the work, but do not rely on knowing the exact current number. Plan pages get updated more often than documentation. Specific sizes and tier availability should be confirmed against the provider's current model page.

The practical framing that matters most: the context window is the model's working memory. Everything you have discussed in this session so far, every tool result, every decision, every artifact that the model is still aware of, lives in the same budget. When the budget fills up, something has to happen. Continuing to chat past the ceiling is not an option.

This turns the mechanical question "how big is my window" into a practical one: "how am I going to manage my working memory across a long session." There are three plays, and each is right in different situations.

The middle play, letting the client compress, is the one most people encounter without naming it. Every major client begins trimming and summarizing older turns once a conversation approaches the ceiling. It happens silently, and the behavior varies between clients, but the pattern is the same: recent turns stay verbatim, early turns lose fidelity, and the model is left reasoning over a summary of its own history instead of the full record.

This is usually fine for ongoing work where the early turns are exploratory scaffolding. It is not fine when a decision or a definition from early in the session is load-bearing for what you are doing now. If something matters long enough to survive an hour of conversation, it should be written to memory, not left in the chat to be compressed.

Modern frontier clients have started shipping some memory of their own. Claude, ChatGPT, and similar clients will remember small facts about you personally across sessions inside that client. This is useful for preferences and long-running style notes. It is not built for enterprise data and is not governed by your company's policies.

Plexara's memory subsystem sits above client-side memory and is scoped to a workspace. It is semantically searchable, governed by access controls, and connected to the catalog, so a brand-new session can retrieve the relevant prior memories, facts about your data, decisions made yesterday, curated artifacts, and re-inject them as context without you having to name them. This is covered in detail in the 207 lesson.

Use context while it fits. Let it compress if the thread is still live but the window is getting tight. Clear and start fresh when you are moving to something else, and trust memory to bring back what matters. The balancing act is small once you have seen the three plays side by side.

Six terms cover most of the vocabulary you will see in client documentation and in discussions about session strategy.