Tag: llm
All the articles with the tag "llm".
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FEMS Project #3 — Streamlit Comparison Dashboard + 3-Backend Question Evaluation (Claude 9 / exaone 7 / gpt 7)
Put a Streamlit dashboard on top of FEMS RAG and threw the same question at 3 backends (exaone3.5:7.8b / claude-opus-4-8 / gpt-4o) simultaneously to compare. Q1 (air compressor anomaly in May) — exaone and gpt said 'no data', while Claude inferred 'weekend-hours anomaly' from summary stats alone without raw data + disclosed its limitations. Q2 (savings measures from a manager's perspective) — Claude 9/10 (incomplete due to token truncation), exaone 7/10 (broken index), gpt 7/10 (concise but hallucinated 'capacitor'). Results of 5-axis scoring.
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MCP Study #1 — Getting Started with Model Context Protocol · First Call with stdio Server + Inspector
Starting to study MCP (Model Context Protocol) today. It's a standard protocol created by Anthropic that standardizes how LLMs access external systems. Comparing it with Tool Use → building my first MCP server (say_hello / add_numbers) with the Python SDK → verifying the stdio connection with MCP Inspector → building a fake energy management server that connects with FEMS. Connection failure due to venv python.exe path issue + fix.
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The Full Flow of RAG Data Preparation — Selection, Cleansing, Chunking, Metadata, and Evaluation Sets
While working on the FEMS project, I got curious about 'what and how should go into a vector DB.' Key insight — data should be selected backward from 'questions that need to be retrieved,' not from the 'domain' criterion. Also covers cleansing / the effect of metadata (document title, section path) before chunks / OCR preprocessing for analog data / building an evaluation set — the full flow of the RAG data pipeline.
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FEMS Project #1 — Comparing Low-Spec Local Setup (Ollama + bge-m3 + Chroma) vs Claude API RAG
Building a RAG prototype while studying the FEMS (Factory Energy Management System) domain. Comparing local LLM inference (Ollama) on a low-spec environment (GTX1660 Super, 6GB VRAM) against calling the Claude / OpenAI APIs. Using bge-m3 for embeddings (strong Korean support) and Chroma as the vector DB. Ollama's cold start of 95 seconds dropped to 10 seconds after warm-up, with accuracy matching the cloud.