1046 posts · Page 16 of 88. ← Blog
A retriever finds the relevant Nodes; a query engine turns those Nodes into a grounded answer. Together they are the RAG pipeline — and the seams between them (postprocessing, response synthesis) are where you tune quality.
A retriever finds the relevant Nodes; a query engine turns those Nodes into a grounded answer. Together they are the RAG pipeline — and the seams between them are where you tune quality.
Agents and tasks are the pieces; the crew is what assembles them into a working team, and its process — sequential or hierarchical — decides whether they run like an assembly line or a delegating manager.
The crew assembles agents and tasks into a working team, and its process — sequential or hierarchical — decides whether they run like an assembly line or a delegating manager.
The model platform decision is usually decided before you compare models at all — by which cloud you're already on, what governance you need, and whether you're renting inference or running it — and getting that framing right matters more than any benchmark.
The model-platform decision is usually settled before you compare models — by which cloud you're on, what governance you need, and whether you're renting inference or running it.
A single consumer reading a topic is easy; the elegant part is how Kafka lets a group of consumers share the work automatically, rebalance when members come and go, and each remember exactly where it left off.
The elegant part of Kafka is how a group of consumers shares the work automatically, rebalances when members come and go, and each remembers exactly where it left off.
The technique that produced superhuman game-playing — a system improving by competing against copies of itself — has an LLM analogue: models that generate their own training signal and bootstrap their way up without new human labels.
The technique that produced superhuman game-playing has an LLM analogue: models that generate their own training signal and bootstrap up without new human labels — and the reward-source problem at its center.
DSPy's optimizer improves whatever your metric rewards, which makes the metric the single most consequential thing you write — get it right and compilation makes your program better; get it wrong and it optimizes confidently toward the wrong target.
DSPy's optimizer improves whatever your metric rewards, which makes the metric the single most consequential thing you write — get it right and compilation makes your program better; get it wrong and it optimizes toward the wrong target.
AI systems add attack surface that traditional security does not cover — the model, its prompts, its retrieved context, and its tools are all attackable — and the only way to know you're defended is to threat-model the whole surface and prove it with red-teaming.
AI adds attack surface conventional security misses — model, prompts, retrieved context, and tools are all attackable. Phase 7: threat-model the whole surface and prove it with red-teaming.
Naive RAG trusts whatever it retrieved, which is how it produces confident answers grounded in the wrong documents; self-correcting retrieval adds the step it was missing — checking the results before using them.
Naive RAG trusts whatever it retrieved, which is how it produces confident answers grounded in the wrong documents. Self-correcting retrieval adds the missing step: check the results before using them.
Not all AI work needs an answer this second, and for the work that can wait, batching and asynchronous processing buy meaningfully cheaper tokens in exchange for latency you were not using anyway.
Not all AI work needs an answer this second, and for the work that can wait, batching and asynchronous processing buy meaningfully cheaper tokens in exchange for latency you were not using anyway.
Tool definitions and structured data quietly consume a large share of the context budget, and how you select, describe, and format them shapes both what fits and how well the model uses it.
Tool definitions and structured data quietly consume a large share of the context budget, and how you select, describe, and format them shapes both what fits and how well the model uses it.
Long-running agent work needs a way to report progress without the client holding its breath, and A2A offers two: stream the updates live, or register a webhook and get called back.
Long-running agent work needs a way to report progress without the client holding its breath. A2A offers two: stream the updates live over SSE, or register a webhook and get called back.
A server is only half the story; the client is what connects to it, discovers its capabilities, and turns a model's intent into real tool calls.
Build an MCP client that launches a server, discovers its tools, and drives them from a language model — the core of what every MCP host does internally.