<?xml version="1.0" encoding="UTF-8"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Guides &amp; Tutorials — The Latent Daily</title><description>Step-by-step AI guides and tutorials for beginners, power users and developers.</description><link>https://thelatentdaily.com/</link><language>en-us</language><atom:link href="https://thelatentdaily.com/rss/guides.xml" rel="self" type="application/rss+xml"/><item><title>How to summarize long documents with AI without losing the important parts</title><link>https://thelatentdaily.com/guides/how-to-summarize-long-documents-with-ai/</link><guid isPermaLink="true">https://thelatentdaily.com/guides/how-to-summarize-long-documents-with-ai/</guid><description>A step-by-step method to get accurate summaries of reports, contracts and papers from any AI assistant, including how to check the result for missing facts.</description><pubDate>Sun, 18 Oct 2026 05:48:00 GMT</pubDate><category>Guides &amp; Tutorials</category><category>guides</category><category>summarization</category><category>productivity</category><category>prompting</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>How to transcribe audio on your own computer with Whisper</title><link>https://thelatentdaily.com/guides/transcribe-audio-locally-with-whisper/</link><guid isPermaLink="true">https://thelatentdaily.com/guides/transcribe-audio-locally-with-whisper/</guid><description>Install OpenAI&apos;s open-source Whisper speech recognition model, transcribe recordings offline, choose the right model size and export subtitles in minutes.</description><pubDate>Sun, 18 Oct 2026 05:47:00 GMT</pubDate><category>Guides &amp; Tutorials</category><category>guides</category><category>speech</category><category>local-ai</category><category>open-source</category><category>whisper</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>What is machine learning? A beginner&apos;s guide with everyday examples</title><link>https://thelatentdaily.com/guides/what-is-machine-learning-beginners-guide/</link><guid isPermaLink="true">https://thelatentdaily.com/guides/what-is-machine-learning-beginners-guide/</guid><description>Machine learning explained from zero: how computers learn patterns from examples, the three main types of learning, and where you already use it every day.</description><pubDate>Sun, 18 Oct 2026 05:46:00 GMT</pubDate><category>Beginner&apos;s Corner</category><category>beginners</category><category>machine-learning</category><category>explainer</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>What is generative AI? Text, images and audio explained simply</title><link>https://thelatentdaily.com/guides/what-is-generative-ai-beginners-guide/</link><guid isPermaLink="true">https://thelatentdaily.com/guides/what-is-generative-ai-beginners-guide/</guid><description>A plain-English introduction to generative AI: how chatbots and image generators create new content, what they are good at, and the mistakes to watch for.</description><pubDate>Sun, 18 Oct 2026 05:45:00 GMT</pubDate><category>Beginner&apos;s Corner</category><category>beginners</category><category>generative-ai</category><category>llms</category><category>explainer</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>Few-shot prompting: how to teach an AI model with a handful of examples</title><link>https://thelatentdaily.com/guides/few-shot-prompting-guide/</link><guid isPermaLink="true">https://thelatentdaily.com/guides/few-shot-prompting-guide/</guid><description>Show, don&apos;t tell: how to use examples inside a prompt to get consistent formats, tone and classifications from any language model, with ready-to-use templates.</description><pubDate>Sun, 18 Oct 2026 05:42:00 GMT</pubDate><category>Prompt Engineering</category><category>prompting</category><category>few-shot</category><category>guides</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>Chain-of-thought prompting: getting better answers by asking for the reasoning</title><link>https://thelatentdaily.com/guides/chain-of-thought-prompting-explained/</link><guid isPermaLink="true">https://thelatentdaily.com/guides/chain-of-thought-prompting-explained/</guid><description>Why asking a model to reason step by step improves math and logic answers, how reasoning models changed the technique, and when it is not worth the extra tokens.</description><pubDate>Sun, 18 Oct 2026 05:41:00 GMT</pubDate><category>Prompt Engineering</category><category>prompting</category><category>reasoning</category><category>chain-of-thought</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>Cheat sheet: which kind of AI model to use for which task</title><link>https://thelatentdaily.com/guides/cheat-sheet-choose-the-right-ai-model/</link><guid isPermaLink="true">https://thelatentdaily.com/guides/cheat-sheet-choose-the-right-ai-model/</guid><description>A one-page decision guide: when to use a large model, a small one, a reasoning model, an open-weight model, an embedding model or a specialized model.</description><pubDate>Sun, 18 Oct 2026 05:34:00 GMT</pubDate><category>Cheat Sheets</category><category>cheat-sheet</category><category>models</category><category>decision-guide</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>Prompting cheat sheet: 12 patterns that work with any AI assistant</title><link>https://thelatentdaily.com/guides/cheat-sheet-prompting-patterns/</link><guid isPermaLink="true">https://thelatentdaily.com/guides/cheat-sheet-prompting-patterns/</guid><description>Role, context, examples, format, constraints and self-checks: twelve reusable prompt patterns on a single page, each with a short template you can copy.</description><pubDate>Sun, 18 Oct 2026 05:33:00 GMT</pubDate><category>Cheat Sheets</category><category>cheat-sheet</category><category>prompting</category><category>productivity</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>The math behind AI: what you actually need to know</title><link>https://thelatentdaily.com/guides/math-you-need-for-ai/</link><guid isPermaLink="true">https://thelatentdaily.com/guides/math-you-need-for-ai/</guid><description>Linear algebra, calculus, probability and statistics: which parts of math matter for understanding and building AI, explained with intuition first.</description><pubDate>Sun, 18 Oct 2026 05:28:00 GMT</pubDate><category>Math for AI</category><category>math</category><category>learning</category><category>machine-learning</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>Gradient descent explained with one simple example</title><link>https://thelatentdaily.com/guides/gradient-descent-explained-simple-example/</link><guid isPermaLink="true">https://thelatentdaily.com/guides/gradient-descent-explained-simple-example/</guid><description>The algorithm that trains almost every AI model, explained with a hiker in the fog and a few lines of Python: gradients, learning rates and why training can fail.</description><pubDate>Sun, 18 Oct 2026 05:27:00 GMT</pubDate><category>Math for AI</category><category>math</category><category>gradient-descent</category><category>machine-learning</category><category>python</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>How to use AI coding assistants effectively: a practical workflow</title><link>https://thelatentdaily.com/guides/how-to-use-ai-coding-assistants-effectively/</link><guid isPermaLink="true">https://thelatentdaily.com/guides/how-to-use-ai-coding-assistants-effectively/</guid><description>Get better results from Copilot, Cursor, Claude Code and other coding assistants: give context, work in small steps, review every change and keep tests in the loop.</description><pubDate>Sun, 18 Oct 2026 04:57:00 GMT</pubDate><category>AI Coding</category><category>ai-coding</category><category>developers</category><category>productivity</category><category>best-practices</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>Model Context Protocol (MCP) explained: the &quot;USB-C port&quot; for AI tools</title><link>https://thelatentdaily.com/guides/model-context-protocol-mcp-explained/</link><guid isPermaLink="true">https://thelatentdaily.com/guides/model-context-protocol-mcp-explained/</guid><description>MCP is an open standard for connecting AI assistants to tools and data. How servers, clients and tools work, how to try it, and the security points to know.</description><pubDate>Sun, 18 Oct 2026 04:54:00 GMT</pubDate><category>Agents &amp; Agentic AI</category><category>mcp</category><category>agents</category><category>tool-use</category><category>open-standards</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>Your first LLM API call: a step-by-step guide in Python</title><link>https://thelatentdaily.com/guides/first-llm-api-call-python-guide/</link><guid isPermaLink="true">https://thelatentdaily.com/guides/first-llm-api-call-python-guide/</guid><description>Set up an API key safely, send your first request to a language model in Python, understand messages, tokens and parameters, and handle errors and costs.</description><pubDate>Sun, 18 Oct 2026 04:53:00 GMT</pubDate><category>APIs &amp; SDKs</category><category>apis</category><category>python</category><category>developers</category><category>tutorial</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>Structured outputs and function calling: getting reliable data from LLMs</title><link>https://thelatentdaily.com/guides/structured-outputs-and-function-calling-explained/</link><guid isPermaLink="true">https://thelatentdaily.com/guides/structured-outputs-and-function-calling-explained/</guid><description>How to make language models return valid JSON that matches a schema, how function calling works, and how to validate outputs so your application never breaks.</description><pubDate>Sun, 18 Oct 2026 04:52:00 GMT</pubDate><category>APIs &amp; SDKs</category><category>apis</category><category>json</category><category>function-calling</category><category>developers</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>How retrieval-augmented generation (RAG) works, step by step</title><link>https://thelatentdaily.com/guides/how-retrieval-augmented-generation-works/</link><guid isPermaLink="true">https://thelatentdaily.com/guides/how-retrieval-augmented-generation-works/</guid><description>RAG lets AI answer from your documents. Each stage explained: ingestion, chunking, embeddings, retrieval, reranking, prompting and evaluation, with practical tips.</description><pubDate>Sun, 18 Oct 2026 04:51:00 GMT</pubDate><category>RAG, Search &amp; Vector DBs</category><category>rag</category><category>embeddings</category><category>vector-databases</category><category>developers</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>Vector databases explained: how they work and how to choose one</title><link>https://thelatentdaily.com/guides/vector-databases-explained-how-to-choose/</link><guid isPermaLink="true">https://thelatentdaily.com/guides/vector-databases-explained-how-to-choose/</guid><description>Embeddings, similarity search, indexes like HNSW and the trade-offs between pgvector, Pinecone, Weaviate, Qdrant, Milvus and Chroma for your RAG project.</description><pubDate>Sun, 18 Oct 2026 04:50:00 GMT</pubDate><category>RAG, Search &amp; Vector DBs</category><category>vector-databases</category><category>rag</category><category>embeddings</category><category>developers</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>Fine-tuning an LLM with LoRA: a practical overview</title><link>https://thelatentdaily.com/guides/fine-tuning-llm-with-lora-practical-overview/</link><guid isPermaLink="true">https://thelatentdaily.com/guides/fine-tuning-llm-with-lora-practical-overview/</guid><description>When fine-tuning helps, how LoRA and QLoRA make it affordable, how to prepare data, train, evaluate and deploy an adapter — and when not to fine-tune at all.</description><pubDate>Sun, 18 Oct 2026 04:49:00 GMT</pubDate><category>Fine-tuning &amp; Training</category><category>fine-tuning</category><category>lora</category><category>qlora</category><category>open-weights</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>How to run a local LLM with Ollama: installation to first chat</title><link>https://thelatentdaily.com/guides/run-local-llm-with-ollama-guide/</link><guid isPermaLink="true">https://thelatentdaily.com/guides/run-local-llm-with-ollama-guide/</guid><description>Install Ollama, download an open model, chat in the terminal or a web interface, pick a model that fits your memory and use the local API from your own code.</description><pubDate>Sun, 18 Oct 2026 04:47:00 GMT</pubDate><category>Local &amp; On-device AI</category><category>local-ai</category><category>ollama</category><category>open-weights</category><category>privacy</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>Quantization explained: how big AI models run on small hardware</title><link>https://thelatentdaily.com/guides/quantization-explained-running-big-models/</link><guid isPermaLink="true">https://thelatentdaily.com/guides/quantization-explained-running-big-models/</guid><description>Quantization shrinks AI models by storing weights with fewer bits. How 8-bit and 4-bit formats work, GGUF, GPTQ and AWQ, and the quality trade-offs to expect.</description><pubDate>Sun, 18 Oct 2026 04:46:00 GMT</pubDate><category>Local &amp; On-device AI</category><category>quantization</category><category>local-ai</category><category>inference</category><category>explainer</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>How LLM inference serving works: batching, KV cache and vLLM</title><link>https://thelatentdaily.com/guides/how-llm-inference-serving-works-vllm/</link><guid isPermaLink="true">https://thelatentdaily.com/guides/how-llm-inference-serving-works-vllm/</guid><description>What happens when a server answers thousands of LLM requests: prefill and decode, the KV cache, continuous batching, PagedAttention and the metrics that matter.</description><pubDate>Sun, 18 Oct 2026 04:45:00 GMT</pubDate><category>MLOps &amp; Infrastructure</category><category>inference</category><category>vllm</category><category>mlops</category><category>gpus</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>How much GPU memory does an AI model need? A sizing guide</title><link>https://thelatentdaily.com/guides/gpu-memory-sizing-for-llms/</link><guid isPermaLink="true">https://thelatentdaily.com/guides/gpu-memory-sizing-for-llms/</guid><description>Estimate the GPU memory needed to run or fine-tune a language model: weights, KV cache, activations and overhead, with worked examples for 8B and 70B models.</description><pubDate>Sun, 18 Oct 2026 04:44:00 GMT</pubDate><category>MLOps &amp; Infrastructure</category><category>gpus</category><category>mlops</category><category>inference</category><category>sizing</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>How to build an evaluation set for an LLM application</title><link>https://thelatentdaily.com/guides/how-to-build-an-evaluation-set-for-llm-apps/</link><guid isPermaLink="true">https://thelatentdaily.com/guides/how-to-build-an-evaluation-set-for-llm-apps/</guid><description>The most important tool in AI engineering: how to collect test cases, define success criteria, score outputs automatically and by hand, and run evals on every change.</description><pubDate>Sun, 18 Oct 2026 04:43:00 GMT</pubDate><category>Evals &amp; Observability</category><category>evals</category><category>testing</category><category>developers</category><category>quality</category><author>dario_admin@resolvia-crm.tech</author></item></channel></rss>