Context Engineering for AI Products

If you’ve spent time trying to get an AI feature to work consistently, you’ve probably blamed the prompt. You tweaked the wording, added more specific instructions, tried examples. Output improved slightly, then degraded on edge cases, then surprised you again a week later.

The prompt usually wasn’t the problem. The full context going into the model was.

Context engineering is the practice of designing everything that reaches an LLM — not just the user-facing prompt, but the system instructions, conversation history, retrieved content, and tool outputs that surround it. Getting this right is the difference between an AI feature that works in a demo and one that holds up with real users under production conditions.

What Context Engineering Actually Is

A context window is the total input the model sees on any given call. In production, that window typically contains four components:

System instructions

The persistent instructions that define the model’s role, constraints, and output format. Most teams write these once and never revisit them. As edge cases surface, new instructions get appended. Six months later, the system prompt is three times its original length, with clauses that partially contradict each other. The model follows the most recent or most emphasized instruction — not the most important one.

Conversation history

The back-and-forth with the user. As a session grows, older turns consume tokens without contributing proportionally to the current response. A chat feature that works well in a 5-turn session often produces noticeably worse responses after 20 turns, purely because no one planned how to manage accumulating history.

Retrieved content

Documents, knowledge-base chunks, or external data injected via RAG. How you format retrieved content matters as much as what you retrieve. Poorly delimited chunks cause the model to blur the line between “instruction” and “reference material.” The result is responses that paraphrase your documentation structure instead of answering the user’s question.

Tool results

When a model calls an API or function, the result re-enters the context. Most APIs return far more data than the model needs. A 200-field JSON response injected verbatim eats tokens and adds noise. The model doesn’t filter — it processes everything it sees.

Prompt engineering is writing the instruction. Context engineering is designing the system the instruction lives inside.

Why AI Features Break the Same Way

The system prompt accumulates contradictions

Every new instruction added to fix an edge case has a chance of conflicting with an existing one. Teams don’t notice this until behavior becomes inconsistent in ways that are hard to reproduce. Two instructions that seem compatible in isolation — “always be brief” and “provide full reasoning for financial decisions” — can produce unpredictable output when triggered together.

History grows without strategy

The most common cause of session-length degradation isn’t model quality. It’s unmanaged conversation history. Older turns dilute focus, and a misunderstanding from 15 turns back keeps shaping the current response. Without a truncation or summarization strategy, quality quietly erodes as sessions extend.

Retrieved content fills the window with noise

RAG works well when retrieved chunks are tightly relevant and clearly framed. It breaks when you retrieve too many chunks, when you include full documents instead of relevant paragraphs, or when retrieved content isn’t separated from instructions with explicit delimiters. The model can’t infer what you intended it to focus on.

Techniques That Work in Production

Order system instructions by priority

Put hard constraints first: what the model must always do, what it must never do. Put behavioral guidance second. Style and tone preferences last. This matches how humans process hierarchical instruction sets — and models are trained on human-generated text, so the same logic applies.

Truncate history, don’t just delete it

When trimming conversation history, preserve the most recent turns verbatim and compress older ones into a summary. A compact summary of turns 1–10, followed by verbatim turns 11–15, gives the model meaningful context at a fraction of the token cost of keeping everything.

Delimit retrieved content explicitly

Use clear separators between instruction blocks and retrieved documents. Something like --- START RETRIEVED CONTEXT --- and --- END RETRIEVED CONTEXT --- around injected material. This is a small change that meaningfully reduces the chance the model treats your knowledge base as part of its operating instructions.

Strip tool results before they re-enter the context

If you’re calling an API that returns data you don’t need, filter it before it hits the context window. This is orchestration-layer work, not a model problem. A bit of preprocessing in your backend can remove entire categories of context noise.

These are the patterns we apply when building AI-powered products for Israeli startups and SaaS companies — they live in the integration layer, not in the prompts.

Signs Your Feature Has a Context Problem

If you’re seeing any of these, the issue is likely context design, not model capability:

• Output quality degrades as sessions get longer
• The model intermittently ignores a specific instruction it usually follows
• Adding a new feature breaks unrelated behavior in your AI feature
• Different users get inconsistent responses on identical inputs
• The feature passes your test suite but misbehaves with real user data

A team we spoke with spent several weeks trying different LLMs to fix a summarization feature that kept producing metadata-heavy summaries instead of content summaries. The actual problem was that their retrieved chunks included document headers and indexing metadata, which the model was summarizing as if it were content. Filtering the chunks before injection fixed it — no model swap required.

When to Prioritize This

Context engineering isn’t something you architect in week one. Early on, simpler prompt iteration is faster. But once your SaaS product or AI feature is in production with real users, context quality becomes the main lever for reliability.

The teams that treat their context window as infrastructure — designed once, monitored in production, revised deliberately — spend far less time debugging regressions than teams that iterate prompts reactively.

If your AI feature is in production and output quality is inconsistent, audit your context strategy before switching providers or rewriting prompts. The structure around the prompt is almost always where the problem lives.

---

Yaniv Amrami is founder of quickdev. He has helped Israeli startups design and ship production-grade AI features across SaaS platforms, mobile apps, and enterprise products.