Pineflake

AI — Pineflake

Pineflake covers artificial intelligence with the depth practitioners need: how large language models work, how to choose and deploy the right model for a specific task, where the leading AI agent frameworks differ, and what developments in the field actually mean for the products being built on top of them.

Our AI articles are written for developers, founders, and technical decision-makers who want to understand the landscape clearly—not marketing copy, but honest analysis of capabilities, tradeoffs, and practical use. We cover OpenAI, Anthropic, Google, open-source alternatives, and the infrastructure layer beneath them.

Whether you're evaluating an LLM for a production use case, building with AI agents, or trying to understand where the field is going, the guides below are written to give you a clear, current picture.

21 articles

  • Chain of Thought Prompting Guide

    Chain of thought prompting guide: what it is, why it works, zero-shot vs few-shot CoT, self-consistency, when to use it, and the honest caveats.

    AI · 2026-08-11 · 9 min read

  • Prompt Engineering Techniques

    Prompt engineering techniques that work: being specific, giving context, few-shot examples, structured output, decomposition, and the mistakes to avoid.

    AI · 2026-08-11 · 8 min read

  • Evaluating AI Agent Reliability

    Evaluating AI agent reliability: why it's hard, what to measure, trajectory vs outcome, benchmarks and tools, and the mistakes that give false confidence.

    AI · 2026-08-04 · 9 min read

  • Multi-Agent Systems Explained

    Multi-agent systems explained: what they are, why split work across agents, the common architectures, how agents coordinate, and when a single agent wins.

    AI · 2026-08-04 · 10 min read

  • Tool Use in LLMs Explained

    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

    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)

    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

    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)

    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

    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

    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

    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

    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

    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

    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

    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

  • Reranking Retrieval Results

    How reranking retrieval results sharpens RAG: cross-encoders vs bi-encoders, the two-stage pipeline, top reranker models, latency, and tuning.

    AI · 2026-06-02 · 9 min read

  • Chunking Strategies for RAG

    Chunking strategies for RAG compared: fixed, recursive, semantic, and contextual methods, plus chunk size, overlap, and the mistakes to avoid.

    AI · 2026-06-02 · 9 min read

  • Embeddings Explained

    Embeddings explained for developers: what vector embeddings are, how models learn them, how similarity works, and how to choose a model for RAG.

    AI · 2026-06-01 · 10 min read

  • Best Vector Databases for RAG

    Compare the best vector databases for RAG—Pinecone, Weaviate, Qdrant, Milvus, Chroma, pgvector, and Turbopuffer—on hosting, hybrid search, and cost.

    AI · 2026-06-01 · 11 min read

  • Retrieval Augmented Generation Explained

    A developer's guide to retrieval augmented generation: how RAG pipelines work, the components that matter, when to use it, and how to evaluate quality.

    AI · 2026-06-01 · 13 min read