How to write precise prompts for LLMs with 10M token context in 2026
Let’s answer right away: combine extended context windows, multimodal OCR, and specialized models likeFern,LFM3andBaidu Unlimited OCR 2. This hands'on guide shows exactly how to structure a prompt that extracts, analyzes and synthesizes long documents, images and audio data in a single professional workflow.
Why traditional prompts no longer cut it in 2026
The quantum leap of extended'context models
In 2026, models likeFern 0.1offer a 10 million'token context window " almost an entire book’s worth of novel in a single call. This game'changer for prompt engineering because:
- You can feed entire company policies, style guides or long contracts without truncation.
- Chunk'based retrieval becomes obsolete; the model maintains narrative continuity.
- Multi'step reasoning tasks (e.g., step'by'step legal analysis) become feasible in a single prompt.
The rise of open'weight multimodal models
Liquid AI releasedLFM3, a suite of 6 open'weight 2B'parameter models that handle text, vision, audio and actions. Together withBaidu Unlimited OCR 2, which can analyze a hundred'page document in a single request, you have a full toolkit for professional prompt engineering.
Step 1: Define the goal and context
A good prompt starts with a clear purpose statement and background context. Use explicit language to guide the model on tone, format and depth.
# Example: Executive summary of a 200'page contract
Instructions:
1. Read the contract provided below carefully.
2. Extract key clauses, deadlines and penalties.
3. Write an executive summary in ≤300words.Following these steps ensures your prompts leverage the full 10M token capacity and produce actionable, high'quality outputs.