context

Product truth: If the catalog is inconsistent, an agent’s selections will look arbitrary (“the fallacious shirt,” “the fallacious size,” “the fallacious material”), and trust collapses quickly. Payee truth: Agentic...

fails in predictable ways. Retrieval returns bad chunks; the model hallucinates. You fix your chunking and move on. The debugging surface is small since the architecture is straightforward: retrieve once, generate once, done. Agentic...

In my latest post, I how hybrid search will be utilised to significantly improve the effectiveness of a RAG pipeline. RAG, in its basic version, using just semantic search on embeddings, will be...

, I’ve kept returning to the identical query: if cutting-edge foundation models are widely accessible, where could durable competitive advantage with AI actually come from? Today, I would really like to zoom in on context engineering — the discipline...

Never miss a brand new edition of , our weekly newsletter featuring a top-notch number of editors’ picks, deep dives, community news, and more. As we wrap up the primary month of 2026, it is likely...

, context really is every thing. The standard of an LLM’s output is tightly linked to the standard and amount of knowledge you provide. In practice, many real-world use cases include massive contexts: code...

1. Introduction two years, we witnessed a race for sequence length in AI language models. We regularly evolved from 4k context length to 32k, then 128k, to the huge 1-million token window first promised...

an LLM can see before it generates a solution. This includes the prompt itself, instructions, examples, retrieved documents, tool outputs, and even the prior conversation history. Context has a huge effect on answer quality....