การนำทางโมเดลภาษาขนาดใหญ่สำหรับนักพัฒนา

This lesson introduces the shift from base LLMs to instruction-tuned models, emphasizing the importance of clear, specific prompting and iterative refinement to achieve reliable results. Developers will learn to treat models as literal assistants by using techniques like delimiters, chain-of-thought reasoning, and structured output to improve accuracy and minimize hallucinations.

This lesson introduces the five core prompting primitives—summarizing, inferring, transforming, expanding, and chatbots—that enable developers to build structured AI-driven workflows. You will learn how to manage LLM state, use delimiters for precision, and choose the right techniques to extract data or analyze sentiment effectively.

This lesson explores how LLMs process information through numerical tokens rather than individual characters, explaining why techniques like hyphenation are necessary for tasks requiring granular detail. It also distinguishes between base models, which focus on pattern prediction, and instruction-tuned models, which are optimized through human feedback to act as reliable assistants.

This lesson introduces prompt chaining and task decomposition as superior alternatives to complex, single-prompt workflows. You will learn how to build modular, cost-effective pipelines that improve reliability and debuggability by integrating dynamic data retrieval, safety moderation, and step-by-step logic.

This lesson introduces LangChain as a modular framework for managing LLM interactions, highlighting how abstractions like ChatOpenAI and ChatPromptTemplate help developers overcome the stateless nature of models. You will learn to manage conversation history, configure temperature for deterministic or creative outputs, and use templates to separate logic from content for scalable application development.

This lesson introduces LangChain orchestration, teaching you how to move beyond single prompts by linking components into sequential pipelines and logic-driven router chains. You will learn to build scalable AI applications by mastering modular workflows that connect models, prompts, and external data to handle complex, multi-step tasks.

This lesson explores the Retrieval-Augmented Generation (RAG) pipeline, focusing on how to ingest, split, and vectorize private data to provide LLMs with relevant, up-to-date context. You will learn to use document loaders to standardize various file formats into structured objects and apply effective chunking strategies to ensure semantic meaning is preserved for accurate retrieval.

This lesson explores advanced Retrieval-Augmented Generation (RAG) techniques designed to overcome the limitations of basic semantic search, such as redundancy and context window constraints. You will learn how to implement tools like Maximum Marginal Relevance (MMR), self-querying, and contextual compression to provide the LLM with a more diverse, filtered, and efficient knowledge base.