If Context Engineering Done Right, Hallucinations Can Be Spark of AI Creativity (milvus.io)

Rather than treating LLM “hallucinations” as pure defects, the piece argues they’re often the same imaginative leaps that drive human creativity—and that with good Context Engineering those leaps can be steered, not suppressed. Context Engineering unifies existing practices (RAG, prompting, function calling, MCP) into three layered responsibilities: Instructions (prompts, few‑shot examples) to set direction; Knowledge (documents, code, embeddings) to ground reasoning; and Tools (APIs, function calls) to execute and provide real‑time feedback. When orchestrated, these layers let models explore novel associations while staying verifiable. The article flags concrete long‑context failure modes—context poisoning (DeepMind’s Gemini 2.5 Pokémon example), context distraction/drift (million‑token windows drift around ~100k tokens; Llama 3.1‑405B around ~32k), tool overload (quantized Llama 3.1‑8B failed with 46 tools but succeeded with 19), and multi‑turn collapse (OpenAI o3 fell from 98.1 to 64.1; Microsoft/Salesforce saw ~39% average drops). It recommends six practical mitigations: context isolation, pruning, summarization, offloading, selective RAG, and optimized tool loading. Finally, it stresses infrastructure requirements—scale, low‑latency consumption, and multimodality—and pitches Milvus (open‑source vector DB delivering sub‑10ms retrieval at hundreds of millions/billions of vectors) plus Loon (a forthcoming cloud‑native multimodal data lake) as an example stack to operationalize disciplined Context Engineering.