How to write precise prompts for LLMs with 10M token context in 2026
Here’s the immediate answer: combine extended context windows, multimodal OCR, and specialized models likeFern,LFM3, andBaidu Unlimited OCR 2. This practical guide demonstrates 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 work in 2026
The quantum leap of extended-context models
In 2026, models likeFern 0.1
- You can input entire company policies, style guides, or lengthy 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 six open-weight 2B-parameter models that handle text, vision, audio, and actions. Combined withBaidu Unlimited OCR 2, which can analyze a hundred-page document in a single request, you have a complete toolkit for professional prompt engineering.
Step 1: Define the goal and context
A good prompt begins with a clear purpose statement and background context. Use explicit language to guide the model on tone, format, and depth.
Following these steps ensures your prompts leverage the full 10M token capacity and produce actionable, high-quality outputs.
IMPORTANT: We are in 2026. All temporal references must be current. Do not mention past years such as 2024, 2023, etc. The content must be fresh and relevant to the present. CURRENT CONTEXT (October 2026): Here are some recent trends and news to inspire you: - Mirendil Launches Fern 0.1, Its First Model, With 10M-Token Context: Mirendil released Fern 0.1, its first model, pretrained from scratch with a 10M-token context window. It targets agentic AI R&D work and scores 100% on R... [2026-10-27] - Meet LEANN: An Open-Source Vector Index that Shrinks a 60M-Chunk RAG Index from 201 GB to 6 GB: UC Berkeley’s LEANN vector index stores a pruned graph and recomputes embeddings at query time. It matches HNSW accuracy at 90% recall using 4 GB in... [2026-10-27] - vLLM Semantic Router Releases d3-ultra: A 27B Open Multimodal Decision Model: vLLM Semantic Router released d3-ultra, a 27B open-weight multimodal decision model. It reads text, images, and video once, then returns probabilities ... [2026-10-27] Use this current information as inspiration to create an original and relevant prompt for 2026.