Pineflake

AI — Pineflake — Page 2

Page 2 of 3 in the AI archive.

12 articles

  • Tool Use in LLMs Explained Abstract glowing light trails forming a network pattern, representing an AI language model connecting to external tools and data sources

    Tool use in LLMs explained: the limits it overcomes, the kinds of tools, the reason-act loop behind agents, how to make it reliable, and common mistakes.

    AI · 2026-08-01 · 9 min read

  • Function Calling in LLMs Tutorial Abstract glowing neural network visualization representing an AI language model processing and routing function calls

    Function calling in LLMs tutorial: how the tool-call loop works, a worked code example, writing good tool definitions, and the pitfalls to avoid.

    AI · 2026-08-01 · 9 min read

  • AI Agent Frameworks Compared (2026 Guide) White robotic hand reaching outward, representing AI agents taking action in the world

    AI agent frameworks compared: LangGraph, CrewAI, the AutoGen/Microsoft lineage, vendor SDKs, and specialists—plus tradeoffs, MCP/A2A, and how to choose.

    AI · 2026-06-26 · 13 min read

  • When to Fine-Tune vs Use RAG: A Decision Guide Dense red patch cables in a network patch panel, representing data routing and retrieval pipelines

    When to fine-tune vs use RAG: the knowledge-vs-behavior rule, a side-by-side comparison, when to use each (or both), and the costly mistakes to avoid.

    AI · 2026-06-26 · 9 min read

  • Quantization for LLMs Explained (2026 Guide) Close-up of a circuit board with chips and components, representing hardware-level model compression

    Quantization for LLMs explained: how lower precision shrinks models, the formats (GGUF, AWQ, GPTQ, FP8), how to choose, and the pitfalls to avoid.

    AI · 2026-06-12 · 8 min read

  • Model Distillation Explained Abstract blue and yellow glowing lines on a dark background, evoking knowledge transferring from a large teacher model to a smaller student model

    Model distillation explained: how a small student model learns from a large teacher, soft vs hard labels, real examples, and when to use it.

    AI · 2026-06-12 · 8 min read

  • Synthetic Data Generation for Training Close-up of a microchip with glowing blue circuits, representing the automated generation of synthetic training data for machine learning models

    Synthetic data generation for training: the methods, tools, and a practical pipeline—plus quality filtering and how to avoid model collapse.

    AI · 2026-06-10 · 9 min read

  • Preparing a Fine-Tuning Dataset Rows of illuminated server racks in a data center, representing the large-scale data storage and curation required for fine-tuning machine learning datasets

    Preparing a fine-tuning dataset: the right format, how much data you need, what "quality" means, cleaning and splitting, and the mistakes to avoid.

    AI · 2026-06-10 · 8 min read

  • LoRA vs Full Fine-Tuning Abstract purple and pink gradient forms suggesting the layered structure of neural network adapters in LoRA fine-tuning

    LoRA vs full fine-tuning compared: cost, quality, hardware, and flexibility—how close LoRA gets, and a clear framework for choosing between them.

    AI · 2026-06-09 · 8 min read

  • How to Fine-Tune Open Source LLMs Glowing circuit board shaped like a human brain on a dark blue background, representing neural network architecture in large language model fine-tuning

    How to fine-tune open source LLMs: when to do it, LoRA vs QLoRA, the 2026 toolchain, data prep, a step-by-step workflow, and common mistakes.

    AI · 2026-06-08 · 11 min read

  • Evaluating RAG Quality Analytics dashboard charts representing evaluation metrics

    Evaluating RAG quality with the metrics that matter: retrieval vs generation scores, RAGAS, the RAG Triad, LLM-as-judge, and catching regressions.

    AI · 2026-06-02 · 9 min read

  • Reducing Hallucinations with RAG Abstract neural network representing an AI model grounded in facts

    How reducing hallucinations with RAG actually works: grounding, where it fails, the techniques that lower the rate, and how to measure faithfulness.

    AI · 2026-06-02 · 8 min read