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MCP Deep Dive: The Universal Connector for LLMs

  The "tool-calling" landscape has been a fragmented mess of bespoke adapters and messy permissions. Model Context Protocol (MCP) is the industry’s shift toward a "USB-C moment" for AI—a standardized protocol that allows any LLM client to talk to any data source or toolset without rewriting the integration logic. 1. The MCP Mental Model: How it Works MCP operates on a simple Client-Server architecture. The "Magic" happens because the LLM doesn't actually touch your data; it communicates its intent to the Client, which then executes the request via the MCP Server. MCP Server: The "Provider." It hosts the tools (APIs, DBs, Files). MCP Client: The "Connector." It lives inside your AI app (Claude Desktop, IDE, etc.). Host App: The UI you interact with. 2. The 3 Primitives: Tools, Resources, and Prompts When building an MCP server, you are essentially exposing three types of capabilities: Primitive Function Real-World Example Tools ...
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RAG Evaluation methods

  Evaluating RAG in 2026 is no longer a matter of "checking if it works"—it is a matter of component-level accountability . If your system fails, you must be able to prove whether the retriever failed to find the data or the generator failed to read it. The following is a logical, "no-nonsense" evaluation framework designed for high-accuracy production systems. 1. The RAG Evaluation Triad To evaluate effectively, you must isolate the Retriever from the Generator . I. Retrieval Metrics (The Input) Contextual Precision: Of the $K$ chunks retrieved, how many are actually relevant? High precision reduces "distractors" that confuse the LLM. Contextual Recall: Did the retriever find every piece of information needed to answer the query? If this is low, your embedding model or chunking strategy is broken. Contextual Relevancy: Is the retrieved noise-to-signal ratio acceptable? II. Generation Metrics (The Output) Faithfulness (Groundedness): This is your ha...
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RAG is no longer just a linear chain; it is a modular architecture.

  Implementing RAG in 2026 is no longer about "top-k similarity search." It is an engineering discipline centered on data topology and decision logic . If you are still just chunking text and hitting a vector DB, you are leaving 40% of your model's potential accuracy on the table. Here is the brutal, tactical breakdown of how to choose and deploy these patterns. 1. The Strategy Matrix: When to Use What Don't over-engineer. Match the pattern to the specific failure mode of your data. If your problem is... Use this Pattern The "Pro" Secret Ambiguity: User asks short, vague questions. Multi-Query / HyDE Don't just paraphrase; generate a "hypothetical ideal answer" and embed that . Lost in the Details: Small chunks lack context. Parent Document Retrieval Index small "child" chunks for retrieval, but feed the full "parent" section to the LLM. Disconnected Facts: "How does A affect C?" (A and C are in different files...
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LLM Evaluation -Accuracy,Latency,Performance

  Evaluating an LLM for production is not a "one-and-done" task; it's a balancing act between three conflicting pillars: Accuracy , Performance , and Latency . As a professional in this space, you should be aware that optimizing one often degrades the others (e.g., higher quantization improves latency but can tank accuracy). Here is the no-nonsense breakdown of how to measure these pillars and the frameworks that actually matter. 1. Accuracy: The "Is it Smart?" Pillar Accuracy in LLMs is elusive because "ground truth" is often subjective. You must move beyond simple string matching to semantic and model-based evaluation. Core Metrics Traditional (Lexical): BLEU, ROUGE, METEOR. (Good for translation/summarization, but blind to meaning). Semantic Similarity: BERTScore or Cosine Similarity on embeddings. This checks if the meaning matches, even if the words don't. LLM-as-a-Judge: Using a stronger model (like GPT-4o) to grade a smaller model (li...
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