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A model that scores well in evaluation still has to serve real traffic within a latency budget, isolate tenants, plan for capacity, and enforce safety in the request path — and the guardrails have to be inline, not a filter someone can route around.
A model that scores well still has to serve traffic within a latency budget, isolate tenants, plan capacity, and enforce safety in the request path — inline, not as a filter someone can route around. Phase 5.
The user's question is written to be asked, not to be searched, so the first thing an agentic RAG system should do is turn that question into queries that actually retrieve well.
The user's question is written to be asked, not searched, so the first thing an agentic RAG system should do is turn that question into queries that actually retrieve well.
Since you pay for every input token, the fastest way to cut the cost of a call without changing the model is to send fewer tokens — and most production prompts are carrying far more than they need.
Since you pay for every input token, the fastest way to cut the cost of a call without changing the model is to send fewer tokens — and most production prompts carry far more than they need.
Retrieval is how you put external, current knowledge into a model's window, and doing it well is a context-engineering problem — the goal is not the most documents, but the right ones.
Retrieval is how you put external, current knowledge into a model's window, and doing it well is a context-engineering problem — the goal is not the most documents, but the right ones.
Agents need to exchange more than plain strings — instructions, files, images, structured data, and finished deliverables — and A2A's content model handles all of it with three composable objects.
Agents exchange more than plain strings — instructions, files, images, structured data, and finished deliverables. A2A's content model handles all of it with three composable objects.
Tools let a model act, but resources and prompts are how a Model Context Protocol server feeds it the right context and gives users repeatable ways to invoke it.
Beyond tools, MCP servers expose resources (read-only context by URI) and prompts (reusable templates). What each is for, how they appear on the wire, and how to choose.
Asking a model to check its own work sounds like free improvement, but whether it actually helps depends entirely on where the feedback comes from — and getting this wrong is the most common way self-evolving agents fool themselves.
Self-critique is tempting but dangerous: without a real external signal, models often fail to self-correct and can get worse. Where self-critique works and where it drifts.
You changed the ranking function and the results look different — but are they better? Search quality feels subjective, yet the entire field rests on measuring it rigorously, because you cannot improve what you cannot measure. Evaluating search means quantifying relevance with metrics that respect what users actually care about: not just whether the right documents appear, but whether they appear near the top. This post is about measuring relevance honestly, offline and online.
You changed the ranking and results look different — but are they better? Precision and recall ignore order (fatal for a ranked list), so rank-aware metrics (precision@k, MRR, NDCG) rule. Offline test collections for fast iteration, online A/B tests as ground truth — the same metrics that evaluate RAG retrieval.
Before you can score an LLM, you have to decide what "good" even means for your task — and that choice determines everything downstream. Metrics fall into a few families, from exact string matching to reference overlap to semantic similarity to task-specific checks, each measuring something different and each with blind spots. Picking the wrong metric is worse than no metric: it gives you a confident number that points the wrong way.
Before you can score an LLM you must decide what "good" means — and that choice determines everything. Metrics fall into families (exact match, reference overlap, semantic similarity, task-specific), each measuring something different with different blind spots. Picking the wrong metric is worse than none: it points confidently the wrong way.
Symmetric encryption has a chicken-and-egg problem: to share a secret key securely, you seem to need a secure channel — which is what you were trying to build. Public-key cryptography is the astonishing idea that broke that loop: two mathematically-linked keys where knowing the public one doesn't reveal the private one. It's the foundation of key exchange, digital signatures, and essentially all secure communication over the open internet.
Symmetric encryption has a chicken-and-egg problem: sharing a secret key securely seems to need a secure channel. Public-key cryptography broke that loop — two mathematically-linked keys where the public one doesn't reveal the private. It's the foundation of key exchange and signatures.
In a regulated business, compliance is not paperwork bolted onto the product — it's a set of hard requirements woven through the code, and treating it as an engineering discipline rather than a legal afterthought is what separates companies that scale safely from ones that get shut down. RegTech is the practice of building compliance in, as software.
In a regulated business, compliance is not paperwork bolted onto the product — it's hard requirements woven through the code, and treating it as an engineering discipline rather than a legal afterthought separates companies that scale safely from ones that get shut down.
Every backend engineer relies on the network constantly and understands it vaguely — until a mysterious timeout, a TLS error, or a latency spike forces a reckoning. The layered model of networking is the map that makes those problems legible: each layer does one job, hides the one below it, and fails in its own characteristic way. Learn the layers and the network stops being magic.
Every backend engineer relies on the network constantly and understands it vaguely — until a mysterious timeout or TLS error forces a reckoning. The layered model is the map that makes those problems legible: learn the layers and the network stops being magic.