RAG
RAG stands for Retrieval-Augmented Generation. It is a pattern that gives a language model access to information outside its training data by fetching relevant documents at query time and including them in the prompt. Instead of memorizing facts, the model reasons over retrieved snippets, which makes answers more accurate, current, and traceable. A typical RAG pipeline has four stages. First, documents are split into chunks and converted into embeddings using an embedding model. Second, those embeddings are stored in a vector database. Third, when a user asks a question, the system embeds the query and searches the database for the closest chunks. Finally, the retrieved chunks are added to the prompt as context, and the model generates an answer grounded in that evidence. RAG is especially useful when answers depend on private data, such as internal wikis, support tickets, or product documentation. It also reduces hallucination because the model can cite the retrieved text. Teams often tune RAG by changing chunk size, overlap, reranking algorithms, and query rewriting strategies.
Published 2026-06-12
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Prompt Engineering
GlossaryPrompt engineering is the practice of crafting inputs to a language model so it produces better outputs without changing the model's weights. It covers word choice, structure, examples, constraints, and the order in which information appears. A well-engineered prompt can turn a mediocre response into a precise, actionable one. Effective prompts are usually clear, specific, and formatted. They state the task, define the audience, set the output format, and include any constraints. Adding examples, known as few-shot prompting, helps the model understand patterns that are hard to describe in words. Breaking complex tasks into steps, called chain-of-thought prompting, improves reasoning and arithmetic. Prompt engineering is iterative. You write a prompt, test it on diverse inputs, measure the results, and refine. Tools like the VePrompts Prompt Optimizer can surface issues such as ambiguity, missing constraints, or conflicting instructions. Good prompt engineering is often the fastest way to improve an AI feature before investing in fine-tuning or custom infrastructure.
RAG Pipeline Architect
PromptDesign production-ready Retrieval-Augmented Generation pipelines with advanced chunking strategies, embedding optimization, and hybrid search capabilities for enterprise knowledge bases.
RAG Implementation Expert
SkillBuild production-grade Retrieval-Augmented Generation systems with vector databases, embeddings, and hybrid search.
MODULAR RAG MCP SERVER
MCP ServerA modular RAG (Retrieval-Augmented Generation) system with MCP Server architecture. Using Skill to make AI follow each step of the spec and complete the code 100% by AI.
Prompt
GlossaryThe input text given to a language model to elicit a desired response.