Articles tagged “retrieval”
7 articles
When vector search fails: GraphRAG for complex CX agent queries
Flat vector search breaks on multi-hop CX queries like 'what discount applies to premium customers in California who signed up before 2024?' GraphRAG uses a knowledge graph to answer these correctly.
Context engineering for reliable CX agents
Context engineering is the discipline of deciding what information your AI agent sees, when it sees it, and how it's formatted. Here's how to apply it to production CX systems.
Your Agent's Context Window Is RAM, Not Storage
Most agent failures trace back to one mistake: treating the context window like a database. Here's the RAM model that fixes attention dilution, latency spikes, and ballooning costs.
Memory bugs don't crash. They just give wrong answers.
Memory bugs don't crash your agent. They just give subtly wrong answers using stale context. Here are 5 test patterns to catch them before customers do.
The RAG You Built Last Year Is Already Outdated
RAG has branched into 5 distinct architectures: Self-RAG, Corrective RAG, Adaptive RAG, GraphRAG, and Agentic RAG. Here's when to use each and how to choose.
Your RAG Returns Wrong Answers. Upgrading the Model Won't Help
Most RAG quality problems are retrieval problems, not model problems. Bad chunking, wrong embeddings, and missing re-ranking cause more hallucinations than model capability gaps.
Your RAG Pipeline Is Answering the Wrong Question
Naive RAG scores 42% on multi-hop questions. Agentic RAG hits 94.5%. The difference: letting the agent decide what to retrieve, when, and whether the results are good enough. Build both in TypeScript and Python.
Learn Agentic AI
Weekly. Patterns for shipping agents that work. MCP, scorecards, regression tests, prompts, model comparisons.
