Context Engineering > Prompt Engineering: Why Your Prompts Fail at Scale

Here's a controversial take: prompt engineering is dead. Or rather, it's been absorbed into something bigger.

In 2024, we obsessed over crafting the perfect instruction. In 2026, the winners are the teams who design the entire information environment around their LLM — not just the prompt text.

This is context engineering.

The Shift

Prompt engineering is one component of context engineering, like how HTML is one component of web development.

A Practical Example

Bad prompt engineering:

Summarise this document accurately.

Good context engineering:

[System: You are a document analyst for {company}. You follow {style_guide}.]
[RAG: {retrieved_relevant_sections}]
[Schema: {output_json_schema}]
[History: {previous_summaries_for_continuity}]
[Task: Summarise the following document, maintaining consistency with previous summaries.]

The second approach designs the full context, not just the instruction. It's the difference between asking a random person vs briefing a specialist.

The 5 Pillars of Context Engineering

1. System Architecture

Your system prompt defines the AI's persona, constraints, and capabilities. This is the foundation everything else builds on.

2. Retrieved Context (RAG)

Don't dump everything in. Retrieve the right information at the right time. Quality > quantity in your context window.

3. Tool Integration

Give the model access to APIs, databases, and calculators. Don't expect it to know things — give it ways to look things up.

4. Conversation Memory

Manage what history stays in context and what gets summarised or dropped. Context window management is a core skill.

5. Output Schema

Define exactly what structure you expect. JSON schemas, TypeScript interfaces, and output validators turn unreliable text into reliable data.

The STCO Framework for Context Engineering

I've been using the STCO framework (Situation, Task, Constraints, Output) which maps perfectly to context engineering:

• S (Situation) = Your RAG context + conversation history + system persona
• T (Task) = The specific instruction with clear deliverables
• C (Constraints) = Guardrails, safety rules, format restrictions, negative examples
• O (Output) = Schema definition, expected structure, validation rules

# Context engineering with STCO
context = {
    "situation": {
        "system_prompt": load_system_prompt("analyst"),
        "rag_context": retrieve_relevant(query, top_k=5),
        "history": get_recent_conversation(session_id, max_turns=10)
    },
    "task": "Analyse the Q2 revenue data and identify anomalies",
    "constraints": [
        "Only reference data provided in context",
        "Flag uncertainty explicitly",
        "Do not make predictions beyond the data"
    ],
    "output": {
        "schema": AnalysisReport.model_json_schema(),
        "format": "json",
        "validation": validate_analysis_report
    }
}

Why This Matters Now

Context windows are getting bigger (Gemini 2M tokens, Claude 200K), but bigger windows don't help if you fill them with noise. Context engineering is about signal-to-noise ratio, not raw capacity.

Teams that design their context pipeline — what goes in, what's excluded, how it's structured — are getting dramatically better results than teams that just write longer prompts.

Resources

Full breakdown: Context Engineering: The Next Evolution

If you want data behind these claims, the prompt engineering evidence page has peer-reviewed research on technique effectiveness.

The STCO Framework guide has a complete walkthrough with code examples.

What's your experience? Is your team still tweaking prompts, or have you moved to designing full context pipelines? 👇