RAG vs Fine-Tuning: When to Use Which for Your AI Application
RAG and fine-tuning are two ways to customize AI models, but they solve different problems. Here's when to use RAG, when to fine-tune, and when to use both.
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10 posts found
Building RAG: Why My First Three Attempts Failed
I wanted to build a RAG system for my documentation. Three attempts, three failures. Here's what went wrong each time, why it failed, and what finally worked.
Read MoreRunning Multiple LLMs: My GPU Memory Management Nightmare
I wanted to run multiple LLMs simultaneously on my GPU. Simple goal, right? Wrong. GPU memory management became a nightmare. Here's what I learned the hard way.
Read MoreGemini 2.0 vs GPT-4o: I Tested Both for Real Work
I spent a week testing Gemini 2.0 and GPT-4o side-by-side on real work tasks. Not benchmarks or demos—actual coding, writing, and analysis. Here's what I found, when to use which, and the real costs.
Read MoreFine-Tuning LLMs: The Expensive Truth Nobody Talks About
I spent $2,400 fine-tuning a language model, thinking it would solve my problem. Three months later, I realized I could have achieved 90% of the results with prompt engineering for $0. Here's the expensive truth about fine-tuning that nobody tells you.
Read MoreI Built a Local LLM Chat App—Here's What Actually Works
After trying three different approaches to build a local LLM chat interface, I finally found what works. Here's what failed, what succeeded, and the real performance numbers you won't find in tutorials.
Read MoreWhy 24GB VRAM Is the New Minimum for Serious AI Work
Modern AI models are breaking on 12GB cards. After running local LLMs, training models, and deploying AI systems, I've learned that 24GB VRAM is now the practical minimum for serious AI work. Here's why, and what it means for your hardware choices—comparing RTX 3090, 4090, and A6000.
Machine Learning
GPU
LLM
CUDA
Hardware
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VRAM
AI Hardware
NVIDIA
RTX 3090
RTX 4090
A6000
Deep Learning
GPU Computing
AI Training
Model Inference
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My Home AI Lab Setup — GPU Computing for Local LLMs
From experimenting with speech-to-text to training lightweight predictive models, I've created a personal AI lab at home powered by high-end consumer hardware. The goal? Run local LLMs, real-time voice agents, VLMs, and GPU-accelerated automation workflows without relying on cloud costs.
Read MoreAI Voice Agents for Customer Support Using Asterisk + LLMs
Build production-grade AI voice agents that understand natural language, access knowledge bases, and handle real customer calls. Complete guide with Asterisk, Whisper, LLMs, and RAG—achieving 73% automation with 2.7s response time.
Read MoreMy Home AI Lab Setup — GPU Computing for Local LLMs
A complete breakdown of my personal AI lab—running local LLMs, real-time voice agents, and GPU-accelerated workflows without cloud costs. Hardware specs, software stack, real-world use cases, and why local AI is the future.
Read MoreDive Deeper into LLM
Interested in LLM solutions? Let's discuss how I can help with your project.