Thinking in Tokens: A Practical Guide to Context Engineering I Novus

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TL;DR

Shipping a great LLM-powered product has less to do with writing a clever one-line prompt and much more to do with curating the whole block of tokens the model receives. The craft, call it context engineering, means deciding what to include (task hints, in-domain examples, freshly retrieved facts, tool output, compressed history) and what to leave out, so that answers stay accurate, fast, and affordable. Below is a practical tour of the ideas, techniques, and tooling that make this possible, written in a conversational style you can drop straight into a tech blog.

If this blog post were an image, what would it look like?”— here’s what OpenAI’s o3 model saw — '*'If this blog post were an image, what would it look like?” - Here’s what OpenAI’s o3 model saw.*

Prompt Engineering Is Only The Surface

When you chat with an LLM, a “prompt” feels like a single instruction: “Summarise this article in three bullet points.” In production, that prompt sits inside a much larger context window that may also carry:

A short rationale explaining why the task matters to the business
A handful of well-chosen examples that show the expected format
Passages fetched on the fly from a knowledge base (the Retrieval-Augmented Generation pattern)
Outputs from previous tool calls, think database rows, CSV snippets, or code blocks
A running memory of earlier turns, collapsed into a tight summary to stay under the token limit

Get the balance wrong and quality suffers in surprising ways: leave out a key fact and the model hallucinates; stuff in too much noise and both latency and invoice spike.

Own The Window: Pack It Yourself

A simple way to tighten output is to abandon multi-message chat schemas and speak to the model in a single, dense block, YAML, JSON, or plain text with clear section markers. That gives you:

Higher information density. Tokens you save on boilerplate can carry domain facts instead.
Deterministic parsing. The model sees explicit field names, easier to extract structured answers.
Safer handling of sensitive data. You can redact or mask at the very edge before anything hits the API.
Rapid A/B testing. With one block, swapping a field or reordering sections is trivial.

Techniques That Pay For Themselves

Window packing

If your app handles many short requests, concatenate them into one long prompt and let a small routing layer split the responses. Benchmarks from hardware vendors show throughput gains of up to sixfold when you do this.

Chunk-size tuning for RAG

Longer retrieved passages give coherence; shorter ones improve recall. Treat passage length as a hyper-parameter and test it like you would batch size or learning rate.

Hierarchical summarization

Every few turns, collapse the running chat history into “meeting minutes.” Keep those minutes in context instead of the verbatim exchange. You preserve memory without paying full price in tokens.

‍Structured tags

Embed intent flags or record IDs right inside the prompt. The model no longer has to guess which part of the text is a SQL query or an error log, it’s labeled.

‍Prompt-size heuristics

General rules of thumb:

Defer expensive retrieval until you’re sure you need it
Squeeze boilerplate into variables
Compress long numeric or ID lists with range notation {1-100}.

Why A Wrapper Isn’t Enough

A real LLM application is an orchestration layer full of moving parts:

*Supporting layers that make context engineering work at scale*

All of these components manipulate or depend on the context window, so treating it as a first-class resource pays dividends across the stack.

Cost, Latency, And The Token Ledger

API pricing is linear in input + output tokens. Reclaiming 10 % of the prompt often yields a direct 10% saving. Window packing, caching repeated RAG hits, and speculative decoding each claw back more margin or headroom for new features.

Quality And Safety On A Loop

It’s no longer enough to run an offline eval once a quarter. Modern teams wire up automatic A/B runs every day: tweak the context format, push to staging, score on a standing test set, and roll forward or back depending on the graph. Meanwhile, guardrails stream-scan responses so a risky completion can be cut mid-sentence rather than flagged after the fact.

From Prompt Engineer To Context Engineer

The short boom in “prompt engineering” job ads is already giving way to roles that sound more familiar, LLM platform engineer, AI infra engineer, conversational AI architect. These people design retrieval pipelines, optimise token economics, add observability hooks, and yes, still tweak prompts, but as one part of a broader context-engineering toolkit.

Key Takeaways

Think in windows. The model only sees what fits; choose wisely.
Custom, single-block prompts beat verbose chat schemas on density, cost, and safety.
Context engineering links directly to routing choices, guardrails, and eval dashboards.
Tooling is catching up fast; human judgment still separates a usable product from a demo.
Career growth now lies in orchestrating the whole pipeline, not just word-smithing instructions.

Thinking in Tokens: A Practical Guide to Context Engineering

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