Pineflake

LLMs articles

29 articles

  • Building Applications With LLMs Two developers reviewing code together on a laptop and desktop monitor in a shared workspace

    A field guide to building applications with LLMs: the capability ladder from prompting to agents, architecting around non-determinism, and shipping.

    AI · 2026-10-23 · 16 min read

  • AI Model Evaluation Metrics A person using a laptop displaying an analytics dashboard with numerical metrics and line charts

    Which metrics fit classification vs generation, why BLEU and perplexity mislead, and how LLM-as-judge really performs—biases included.

    AI · 2026-10-23 · 10 min read

  • Choosing an LLM for Your Use Case Laptop displaying source code beside notebooks on a desk, representing the process of evaluating models for a development task

    Choosing an LLM for your use case: define your real requirements, weigh proprietary vs open, test on your own data, and avoid picking by leaderboard.

    AI · 2026-10-10 · 9 min read

  • Small vs Large Language Models Open laptop with a colorful sunset reflection on a dark surface, representing AI model computing

    Small vs large language models: the real tradeoffs in capability, cost, latency, and privacy—when a small model wins and how to decide.

    AI · 2026-09-19 · 9 min read

  • AI Inference Cost Optimization Team collaborating around a laptop in a modern office, representing the engineering decisions behind optimizing AI inference costs

    AI inference cost optimization: the biggest levers—right-sizing the model, cutting tokens, caching, batching, and API vs self-host by volume.

    AI · 2026-09-02 · 9 min read

  • Self-Hosting AI Models Developer working at a laptop in a modern workspace, representing the engineers and infrastructure behind self-hosted AI models

    Self-hosting AI models: why and when to run open models on your own infrastructure, the serving stack, the real costs, and the mistakes to avoid.

    AI · 2026-09-02 · 10 min read

  • Running LLMs Locally Laptop displaying colorful code on a desk, representing local LLM development and running models on personal hardware

    Running LLMs locally: why do it, the tools (Ollama, LM Studio), the hardware and memory that decide what you can run, quantization, and cloud tradeoffs.

    AI · 2026-08-20 · 9 min read

  • Deploying LLMs in Production Modern server racks in a data center, representing the infrastructure required to deploy and operate LLMs in production

    Deploying LLMs in production: managed API vs self-host, right-sizing your model, inference with vLLM, scaling, cost control, and safety.

    AI · 2026-08-20 · 12 min read

  • Chain of Thought Prompting Guide Open notebook and pen on a desk, representing the step-by-step reasoning and worked examples used in chain-of-thought prompting

    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 Close-up of code on a computer screen, representing the structured instructions and developer workflows behind prompt engineering

    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 Data analytics dashboard showing performance metrics and charts on a dark screen, representing systematic evaluation of 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 View from space of a glowing network of light connecting cities across Earth, representing multiple agents communicating and coordinating across a system

    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 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

  • Reranking Retrieval Results Sorted data visualization representing reordered search 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 Lines of code on a screen representing text being segmented into chunks

    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 Abstract geometric points in space representing vectors in high-dimensional space

    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 Rows of database servers representing vector storage infrastructure

    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 Abstract visualization of connected data nodes representing a retrieval system

    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