CallMissed Blog
Insights on AI communication, voice agents, WhatsApp automation, and the future of customer engagement.
6 min readRAG Best Practices in 2026: Chunking, Reranking, Hybrid Search
RAG (retrieval-augmented generation) graduated from a 2023 buzzword to a 2026 production pattern, and along the way the industry agreed on what actually matters. Most quality wins come from four levers: chunking strategy, hybrid retrieval, rerankers, and the long-context vs RAG tradeoff. Get those f…
5 min readKnowledge Graphs vs Vector RAG: When to Use Which in 2026
RAG is the standard pattern for grounding LLMs in private data. The default uses vector search. Knowledge graphs offer a different approach with different trade-offs. How Vector RAG Works Chunk documents, embed them, store in a vector database, retrieve by semantic similarity, and inject into the pr…
Hallucination Detection: Techniques That Actually Work
LLM hallucinations are not going away. Frontier model error rates have improved enormously since 2023, but production deployments still need active hallucination detection because the cost of a confidently wrong answer in a customer-facing context is high. Here is what is actually working in 2026, r…
Tutorial: Build a Production RAG App in 2 Hours
This tutorial walks through building a production-grade RAG (Retrieval-Augmented Generation) app from scratch in roughly two hours. Not a toy — a system with chunking, hybrid retrieval, reranking, eval, and citations. Code samples are Python with widely-used 2026 libraries; substitute whatever you p…
Vector Databases in 2026: Pinecone, Qdrant, Weaviate, pgvector
The vector database market has consolidated. By mid-2026 four products account for the overwhelming share of production RAG and embedding-search workloads: Pinecone, Qdrant, Weaviate, and pgvector. Each represents a distinct philosophy — fully managed serverless, OSS-first with a managed tier, hybri…
Embedding Models in 2026: OpenAI vs Cohere vs Open Source
The choice of embedding model shapes everything downstream in a RAG system — retrieval quality, storage cost, query latency, and ceiling on hybrid-search performance. In 2026 the field has narrowed to a clear set of contenders: OpenAI's text-embedding-3 family, Voyage AI's voyage-3 / voyage-3-large,…
Fine-Tuning vs RAG: The 2026 Decision Framework
"Should we fine-tune or do RAG?" is a question that has lost most of its drama. By 2026 the field has settled on a clear answer: they do different things, and most production systems use both. The interesting question is no longer "which one?" but "what belongs in which?" The single most useful ment…
The Context Window Arms Race: 1M to 10M Tokens
The 2026 context-window numbers look science-fiction at first glance: Llama 4 Scout at 10 million tokens, Claude Opus 4.7 at 1 million (at standard pricing, no premium), Gemini 3.1 Pro at 1 million, Mistral Medium 3.5 at 256K. A single prompt can now hold the equivalent of 15,000 pages of text. The …