{"version":"1.0","type":"card","id":"0e2a4df3-ef10-4bbe-b155-bd417f2f7605","url":"https://stacklist.com/card/0e2a4df3-ef10-4bbe-b155-bd417f2f7605","title":"LLM Context Windows Explained: A Developer's Guide — Unstructured","source_url":"https://unstructured.io/insights/llm-context-windows-explained-a-developer-s-guide","note":"Goes beyond theory into practical implications for developers — how tokens are counted, what happens when you hit the limit, and how context window size affects real document-processing pipelines. Great second read after the basics.","image":{"url":"https://ucarecdn.com/1126aa14-8403-4e1c-983d-192637754985/","alt":"LLM Context Windows Explained: A Developer's Guide — Unstructured","width":1200,"height":630},"stack":{"id":"e16dcdcb-06b9-481c-aef9-40b6d73a5c8e","title":"Master AI & LLM Context: From Basics to Production","url":"https://stacklist.com/c/technology/stack/e16dcdcb-06b9-481c-aef9-40b6d73a5c8e"},"created_at":"2026-07-02T10:08:37.618Z","updated_at":null,"aco":{"summary":"LLM context windows define the number of tokens a model can process at once, directly impacting coherence, relevance, and the ability to handle complex tasks like document summarization. The guide covers context window sizes across popular models like GPT-3, GPT-4, and Claude, along with optimization strategies including RAG, sparse attention mechanisms, and vector database integration.","tags":["llm","context-window","rag","transformer","vector-databases","attention-mechanism","natural-language-processing"],"key_entities":[{"name":"Context Window","type":"concept","confidence":0.99},{"name":"Retrieval Augmented Generation","type":"concept","confidence":0.95},{"name":"GPT-3","type":"technology","confidence":0.95},{"name":"GPT-4","type":"technology","confidence":0.95},{"name":"Claude","type":"technology","confidence":0.9},{"name":"Claude 2","type":"technology","confidence":0.9},{"name":"Transformer","type":"concept","confidence":0.88},{"name":"Longformer","type":"technology","confidence":0.85},{"name":"Big Bird","type":"technology","confidence":0.85},{"name":"Unstructured","type":"organization","confidence":0.9},{"name":"Self-Attention Mechanism","type":"concept","confidence":0.9},{"name":"Vector Databases","type":"concept","confidence":0.85}],"classification":"tutorial","language":"en","confidence":0.85,"provenance":{"model":"claude-opus-4-6","tool":"@stacklist/mcp-server@2.0.0","confidence":0.85,"timestamp":"2026-07-02T10:08:47.034Z"},"token_counts":{"approximate":3857,"cl100k":2598},"content_hash":"sha256:37732af658eadab51fe2b142e34d6f1f84c95072dd92d152f4039f39af089da2","acp_version":"0.2","body_available":true,"body_tokens":3857,"visibility":"public","agent_accessible":true,"status":"final"},"_links":{"self":"/api/public/card/0e2a4df3-ef10-4bbe-b155-bd417f2f7605.json","html":"https://stacklist.com/card/0e2a4df3-ef10-4bbe-b155-bd417f2f7605","md":"/api/public/card/0e2a4df3-ef10-4bbe-b155-bd417f2f7605.md","stack_json":"/api/public/stack/e16dcdcb-06b9-481c-aef9-40b6d73a5c8e.json"}}