What a 2-Million-Token Context Actually Changes
Context windows keep growing. Here's what genuinely improves — and what still doesn't.
Topic
Retrieval & RAG
Grounding models in external knowledge, reliably.
Native Multimodal Generation: One Model, Many Media
Frontier assistants increasingly read and *create* across text, images, and video in one model.
Lost in the Middle: Long Context's Weak Spot
Models handle huge context windows — but pay less attention to what's buried in the middle.
How to Evaluate a RAG System
A RAG system has two places to fail — retrieval and generation. Measure both.
Semantic Caching: Reuse Answers, Cut Costs
Two users ask the same thing in different words. Why pay the model twice?
Vector Search at Scale: HNSW and ANN, Explained
How do you find the nearest vectors among billions in milliseconds? Approximation.
How to Actually Measure Hallucination
'The model hallucinates less' is a claim. Here's how you turn it into a number.
Sparse vs Dense Retrieval, Explained
Keyword matching and vector search are two different tools. Knowing both makes better search.
Multimodal Embeddings: Text and Images in One Space
Search images with words, or find text about a picture — by putting both in the same space.
Knowledge Graphs Meet LLMs
Structured facts and fluent language models each fix the other's biggest weakness.
AI for Legal Work: Contracts and Research
Law is drowning in documents — exactly the problem AI is good at, with caveats.
Answer Engines: The New Front Door to the Web
People increasingly ask an AI instead of searching — and it changes how content gets found.
Fine-Tuning Embeddings for Your Domain
Generic embeddings are good; embeddings tuned to your data can be much better.
Fine-Tuning vs RAG vs Prompting: When to Use Each
Three ways to make a model do what you want — and they solve different problems.
What Are Embeddings, Really?
The idea that turns words, images, and meaning into numbers you can search.
The Million-Token Context Is Now Standard
Context windows exploded. What actually changed is subtler than 'paste everything in.'
The Model Context Protocol (MCP), Explained
A standard way to plug tools and data into AI — the 'USB-C for models.'
Hybrid Search: Keywords + Vectors
Semantic search is powerful, but it misses exact matches. Combining both wins.
Multimodal RAG: Retrieving Images, Not Just Text
When your knowledge lives in charts, diagrams, and screenshots, text-only retrieval isn't enough.
Vector Databases: What They Are and Why
The storage engine behind semantic search and RAG.
Chunking Strategies for RAG
How you split your documents quietly decides how good your retrieval is.
How to Build a Chatbot That Doesn't Hallucinate
You can't make a model perfect — but you can build a system that rarely makes things up.
Re-ranking: The Underrated RAG Upgrade
A second pass that reorders retrieved results by true relevance — often the biggest quality win.
Agents Need Memory, Not Just Retrieval
Stateless lookups aren't enough for long-running agents. Persistent memory is the next layer.
Teaching Agents to Retrieve
Instead of hand-writing retrieval logic, train the agent to discover it.
RAG Grew a Reasoning Loop
Retrieval stopped being a single step and became think, retrieve, rethink, act.
Graph-RAG: Beyond Vector Search
Some questions need relationships, not just similar chunks.
Long-Term Memory for LLMs
Persistent, temporally-chained state — not a fresh lookup every time.
Context Engineering Is the New Prompt Engineering
The craft moved from wording the question to assembling what the model sees.
Is Long Context Killing RAG? No — It Grew Up
Million-token windows didn't make retrieval obsolete. They changed what it's for.
RAG Is Not Dead — It Grew Up
Long context didn't kill retrieval. It changed what retrieval is for.