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8 min readLLM Temperature and Sampling Explained: What Each Setting Actually Does
Temperature 0 gives deterministic output. Temperature 1.0 adds variety. Above 1.0, output degrades. Here is what temperature, top-p, and top-k actually control.
9 min readRAG vs Fine-Tuning: Which One Does Your Application Actually Need?
Most teams fine-tune when they should be using RAG. RAG handles knowledge. Fine-tuning handles behavior. Here is the decision framework to tell them apart.
6 min readLLM Context Management: How to Handle Long Conversations Without Losing Quality
Conversation quality degrades as context fills. Five concrete strategies prevent this: sliding windows, summarization, RAG memory, explicit tracking, and stateless design.
4 min readGemini 1.5 Pro vs GPT-4o: Which Is Better in 2026?
Gemini 1.5 Pro and GPT-4o are the two dominant general-purpose LLMs in 2026. Here is a direct benchmark-by-benchmark breakdown to help you pick the right one.
6 min readLlama 3.3 Complete Guide: Meta's Best Open Source LLM
Llama 3.3 70B is Meta's most capable open source model, delivering GPT-4-class performance you can run locally or deploy without per-token API fees.
5 min readMistral AI Models Guide: Which One to Use in 2026
Mistral AI offers a lineup from efficient 7B models to GPT-4o-competitive flagship models, all at significantly lower prices than OpenAI. Here is how to choose.
5 min readPhi-3: Microsoft's Small LLM That Punches Above Its Weight
Microsoft's Phi-3 family delivers surprising capability from tiny parameter counts. Phi-3 Mini at 3.8B parameters runs in 4GB of VRAM with MMLU scores that embarrass models three times its size. Practical deployment guide with benchmarks and honest tradeoffs.
8 min readFunction Calling in LLMs: How to Get Structured Actions From AI
Function calling gives LLMs a structured way to request execution of specific functions with typed parameters, eliminating the need to parse free-form text outputs.
7 min readStreaming LLM Responses: How to Build Real-Time AI Interfaces
Streaming makes AI interfaces feel dramatically more responsive by showing users tokens as they generate rather than making them wait for a complete response.
8 min readLLM Embeddings Explained: What They Are and How to Use Them
Embeddings convert text into dense numerical vectors that capture semantic meaning, enabling similarity search and retrieval at scale without running inference on every query.
8 min readDeepseek: The Open Source LLM That Changed the Cost Equation
Deepseek trained a GPT-4o-competitive model for a reported $5.6M - roughly 1/20th of comparable frontier model training costs - and released it under MIT license.
7 min readMultimodal LLMs: Working With Text, Images, and Audio Together
Multimodal LLMs process text, images, audio, and video in a single model, enabling use cases like document analysis, chart understanding, and audio transcription without separate pipelines.