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RAG and fine-tuning solve different problems. RAG pulls external text into the prompt at query time, so the model answers from documents you can edit or cite. Fine-tuning retrains the model’s weights until a skill, format, or tone is built...
Self-hosting inference pays off in two cases: you push past roughly two million tokens a day, or your data legally cannot leave your network. Below that line a hosted API is cheaper, and it spares your team from running GPUs at all. For mos...
AI coding tools stopped competing on autocomplete and started competing on autonomy. Agents moved into the terminal and the cloud. Every major vendor shipped its own model. SWE-bench cleared 80% and bunched up, flat-rate plans gave way to m...
cognee, Mem0, Zep, and Letta all give an AI agent memory that survives past a single conversation, in four different ways. Mem0 extracts facts into a store. Zep builds a temporal knowledge graph. Letta lets the agent edit its own memory. co...
LLM observability means giving your app a black box, the same idea as an airplane’s flight recorder: something that captures every step of a request so you can replay exactly what happened when the output is wrong.
🧭 Part 9 of the RAG &...
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