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24 results for “RAG” · keyword match
A search tool turns post-cutoff questions from hallucination bait into retrievable facts, and it brings 3 problems with it: choosing sources, paying for pages that become tokens, and reading text that may be aimed at the model rather than the user. This is the wiring guide: the
Most tutorials get you a working agent in ten minutes and skip what breaks it in week two. Sixteen lessons covering the loop, tool descriptions, memory, fallbacks, guardrails, evaluation, and the cost levers that decide your bill.
Same model, same tools, same prompt, 2 different machines: one answers when you type, the other triages your inbox at 6am and escalates what it couldn't handle. The difference is supervision, and removing it changes everything: latency stops mattering, durable state starts, and
Every prompt containing personal data becomes 5 copies of that data: provider logs, your traces, caches, eval exports, bug reports. This is the redaction architecture that actually holds: a single boundary function, layered detectors, consistent pseudonyms like [PERSON_1] that
A 500-token answer takes 8 seconds either way; streaming decides whether the user reads for 8 seconds or stares at a spinner for 8 seconds. This is the mechanism: time to first token and cadence as the 2 numbers that define perceived speed, the buffering layers that silently
A RAG bot is only as current as its last ingest, and re-embedding the whole corpus nightly pays for thousands of unchanged documents to pick up a handful of edits. This is the incremental alternative: hash to detect changes, upsert by stable chunk IDs, tombstone deletes, and
A text-to-SQL agent turns 'how many customers churned?' into a real query against a real database. The models are good enough to try this in 2026, but accuracy is mostly a schema problem and safety is entirely a database problem. This is the loop that makes generation reliable,
Classic RAG runs 1 fixed retrieval pass and hopes the chunks are right. Agentic RAG puts a model in charge of the search itself: rewriting queries, grading what came back, and retrying until the evidence covers the question. This is the mechanism behind the loop, the modeled 2
Naming a document is not the same as quoting it. This is the mechanism behind a real inline citation, the specific difference between a citation that is correct and one that is faithful, and why a confidently-wrong citation does more damage than an honest gap. Citations reduce
Most agent pipelines run every step on the same frontier model out of habit, not need. This is the decision framework for picking a small model per step instead of per pipeline: whether the output space is narrow, whether the task needs multi-hop reasoning, whether you can
The tool you pick is not what decides the risk, the autonomy level is. This is a tool-agnostic, 4-level framework for how much a coding agent should actually be allowed to do, from a diff that waits for your approval to a merge and deploy with no human step at all, grounded in
Prompt engineering wrote the instructions. Context engineering decides what an agent actually sees on any given turn: which tools stay in scope, which documents survive the cut, and when a long run needs to compact its own history or spin off a sub-agent instead of dragging
Real production traces are the best eval data you can get, and most teams do not have enough of them yet. Here is the concrete mechanism for generating a synthetic eval set from a handful of real examples, plus the honest failure modes: data that drifts from what real users do,
Prompting, supervised fine-tuning, and reinforcement learning are 3 different ways to make a model better at your task, and they are not interchangeable. Here is what each one actually changes, the 2026 shift that just narrowed the fine-tuning lane at OpenAI and Google, and the
OCR-then-parse converts a page to text outside the model, so a language model only ever sees what the OCR engine decided to keep. A vision-language model reads the page itself, pixels and text together, which is why it can answer a question about a specific box on the page while
Constrained decoding is the mechanism behind OpenAI's Structured Outputs and Anthropic's strict tool use: it compiles your JSON Schema into a grammar and masks invalid tokens before they can ever be sampled, so shape failures stop happening. What it cannot do is check that the
Apple and Google both ship a language model built into the phone itself, callable through a platform SDK at zero marginal cost per call, fully offline, with data that never leaves the device. Here is what Apple's Foundation Models framework and Google's Gemini Nano actually do,
Model distillation trains a brand-new small model to imitate a larger teacher model's outputs on one narrow task, so production runs on the cheap model instead of paying frontier prices for every call. Here is the actual mechanism, what OpenAI's Model Distillation feature and
An LLM gateway is a proxy layer that gives your app one stable endpoint across several model providers, then decides which one answers each request. Most of what ships as "smart routing" is really 2 jobs, automatic fallback and cost-based rules, not model choice driven by
Installing a community MCP server means running someone else's code with your credentials, and nothing resembling an app-store review checks it first. Here is what the research and the official spec actually document about tool poisoning, supply-chain risk, confused-deputy
When an agent calls a tool on a user's behalf, most systems today can only see the agent, not the person. Here is what RFC 8693 token exchange and the newly shipped ID-JAG pattern actually do about that, and why a single shared credential stops working the moment more than one
A long-running agent that crashes at hour 6 either starts over from zero or resumes from its last checkpoint, depending on whether you built it with durable execution. Here is what checkpointing and replay actually do, how Temporal, Inngest, DBOS, and Restate differ, and what it
Realtime voice AI agents split into 2 real architectures: a cascaded pipeline of 3 chained models, or a single native speech-to-speech model. This piece grounds the tradeoff in OpenAI, Google, and Amazon's own documented specifics, not vendor marketing.
Graph retrieval builds an entity-and-relationship graph out of your documents and walks it at query time instead of ranking chunks by similarity. On a published benchmark it beat reranked vector search by 10.5 points on complex reasoning and 13.1 on contextual summarization, and
