Tag: LLM
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Function Calling vs RAG: 2.3s Latency Gap in Production
Compare Function Calling vs RAG performance in production systems. Discover why the 2.3s latency gap matters and which approach fits your use case.
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LoRA vs Full Fine-Tuning: Cost-Accuracy Trade-offs
LoRA cuts fine-tuning cost 6.5x but loses 2-3% accuracy. Here's when that trade-off breaks your interview demo โ with GPU memory benchmarks.
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LLM Context Windows: Why 128K Tokens Break at 50K
Discover why LLM context windows fail before their limits and learn proven techniques to maximize token usage in production applications.
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LoRA vs QLoRA vs Full Fine-tuning: GPU Memory Benchmarks
Full fine-tuning costs $5/hr on A100. QLoRA drops it to $0.50 on T4 โ with matching accuracy at rank 64. Real memory breakdowns + 47-run benchmark.
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GPT-4 vs Claude 3.5 vs Gemini: MMLU Zero-Shot Accuracy
GPT-4 beats Claude 3.5 by just 1.8% on zero-shot MMLU โ way closer than official benchmarks claim. Real accuracy numbers from 1,000 questions.
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FlashAttention-2 Warmup: Fix 3x Slower First Batch
First FlashAttention-2 batch is 3x slower? Fix kernel compilation overhead with warmup, persistent cache, and bucketingโreal latency numbers included.
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LangChain vs LlamaIndex: RAG Latency on 10K Documents
LangChain vs LlamaIndex on 10K docs: 3x latency gap, 2x memory difference. Real query benchmarks reveal which RAG framework wins.
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MCP Tutorial: Build an AI Tool Server in Python
Learn how to build MCP (Model Context Protocol) servers in Python. Step-by-step tutorial with real code examples for tools, resources, and prompts. The open standard backed by Anthropic, OpenAI, and Google.