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How 1M Token Context Actually Changed My Daily Workflow

Sergii Potapov

— 2 min read
How 1M Token Context Actually Changed My Daily Workflow

Not theory. Here's exactly how I use it.

TL;DR

GPT-5.4 and Claude Sonnet 4.6 both shipped with 1 million token context windows this week. I've been testing them in real work — research, writing, code review. Here's what actually works, what doesn't, and the prompts I'm using.

The Promise vs Reality

The hype: "Feed entire codebases! Analyze whole books! Never lose context!"

The reality: More nuanced. 1M tokens is roughly 750,000 words — yes, that's an entire book. But throwing everything at the model doesn't automatically make it smarter.

Here's what I learned in 3 days of real use.

What Actually Works

1. Research Synthesis (My Killer Use Case)

The workflow:

  1. Fetch 15-20 sources on a topic (articles, papers, discussions)
  2. Paste them all in a single context
  3. Ask for synthesis, not summary

The prompt that works:

I've included {N} sources about {topic}.

Don't summarize them individually. Instead:
1. Find the 3-5 key insights that appear across multiple sources
2. Identify contradictions or debates between sources
3. Note what's missing — what questions aren't being answered?
4. Give me your synthesis in 500 words max.

Sources matter more than quantity — cite specific ones when making claims.

Why this works: The model can actually cross-reference. Before 1M context, I'd have to manually track which source said what. Now it just... knows.

Real example: Yesterday I researched "AI agent reliability" across 18 sources. The model spotted that academic papers focused on failure modes while industry content focused on success stories — a meta-insight I would have missed manually.

2. Code Review With Full Repo Context

The setup:

find . -name "*.py" -exec cat {} \; | head -c 500000

The prompt:

This is a Python codebase for {project description}.
I'm adding a new feature: {feature}.
1. Which existing files will I need to modify?
2. What patterns does this codebase use that I should follow?
3. Are there any potential conflicts with existing code?
4. Write the new code, matching the existing style.

Why this works: The model sees how the codebase actually works, not just the file I'm editing.

3. Document-Heavy Analysis

Use case: Analyzing long contracts, reports, or documentation.

This is a {document type}, {X} pages.
I need to understand:
1. {Specific question 1}
2. {Specific question 2}
For each answer, quote the exact section you're referencing.

What Doesn't Work (Yet)

❌ "Just figure it out" — Vague prompts don't improve with more context.

❌ Needle-in-haystack retrieval — Slower than Ctrl+F for specific sections.

❌ Token-stuffing — 200K relevant tokens > 800K "maybe useful" content.

Rule of thumb: Quality of context > quantity of context.

Cost Reality Check

1M tokens of input ≈ $3-15 depending on model. My spend: ~$5-10/day. ROI is obvious.

Try It Yourself

  1. Pick one research task you do manually
  2. Gather 10+ sources
  3. Paste them all into Claude or GPT-5.4
  4. Use the synthesis prompt above
  5. Compare time spent vs quality

The unlock isn't "more tokens = better." It's "relevant context + specific questions = synthesis you couldn't do before."

What's your best use case for long context? Reply and I'll feature the best ones.

P.S. This article was written using this workflow. 18 sources, synthesized in one pass. 35 min vs 2+ hours.

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