RAG on ByF (BaiFan) · Daily Deep Think

The Classifier Is the Savior Again

blog@baifan.site (ByF (BaiFan)) — Sun, 20 Sep 2026 13:00:00 +0800

In any enterprise tech team that claims to be “fully AI-powered,” the bill nobody wants to look at closely is usually not production inference — it’s the evaluation pipelines we run to deceive ourselves.

Over the past three years, the biggest, most sacred, and least defensible totem in the enterprise stack has been the “private enterprise knowledge base” — first repackaged as RAG, then upgraded again as GraphRAG with a knowledge-graph label. Every architect, when drawing the final architecture diagram for the CTO, dutifully adds the last box: “Closed-loop evaluation and continuous alignment.”

TencentDB Agent Memory: Real Layered Memory for AI Agents

blog@baifan.site (ByF (BaiFan)) — Tue, 03 Feb 2026 14:00:00 +0800

Why AI Agents Need a Memory System
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If you’ve spent serious time with AI agents, these scenarios will feel familiar:

Re-explaining the project background, coding conventions, and output format preferences in every new session
Halfway through a long task, the context fills up with tool logs and the agent starts “forgetting” earlier instructions
Wanting the agent to learn your working habits, but having no way to do it except hand-writing a system prompt every time

Memory isn’t about making the AI store everything — it’s about making sure humans never have to repeat themselves. That’s the core idea behind TencentDB Agent Memory.

RAG Complete Guide: Build a Local RAG System from Scratch

blog@baifan.site (ByF (BaiFan)) — Wed, 27 Aug 2025 14:00:00 +0800

Building a Local RAG with LangChain + Ollama + pgvector: A Complete Hands-On Walkthrough from 0 to 1 (with uv Dependency Management and an Interview Guide)
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This post is a directly actionable document. Follow it top to bottom and you will build a local RAG (retrieval-augmented generation) system from scratch and understand the key concepts and code. All core scripts come with comments to make learning and interview review easier.

Prompt Engineering: From Prompts to Context Engineering

blog@baifan.site (ByF (BaiFan)) — Wed, 13 Aug 2025 14:00:00 +0800

AI Tutorial: Prompt Engineering
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Prompt engineering focuses on designing, optimizing, and strategizing prompts — helping users mobilize the capabilities of large language models more effectively, and pushing their adoption across real-world scenarios and research domains.

1. Basic Concepts
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1.1 What Is Prompt Engineering
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A prompt is simply this: you use natural language to tell the model what to do, how to do it, what it may do, and what it must not do. That is all there is to it.

RAG on ByF (BaiFan) · Daily Deep Think

The Classifier Is the Savior Again

TencentDB Agent Memory: Real Layered Memory for AI Agents

Why AI Agents Need a Memory System #

RAG Complete Guide: Build a Local RAG System from Scratch

Building a Local RAG with LangChain + Ollama + pgvector: A Complete Hands-On Walkthrough from 0 to 1 (with uv Dependency Management and an Interview Guide) #

Prompt Engineering: From Prompts to Context Engineering

AI Tutorial: Prompt Engineering #

1. Basic Concepts #

1.1 What Is Prompt Engineering #

Why AI Agents Need a Memory System
#

Building a Local RAG with LangChain + Ollama + pgvector: A Complete Hands-On Walkthrough from 0 to 1 (with uv Dependency Management and an Interview Guide)
#

AI Tutorial: Prompt Engineering
#

1. Basic Concepts
#

1.1 What Is Prompt Engineering
#