AI Concepts 𧠶
A concise overview of key concepts and building blocks in modern AI development.
Large Language Models (LLMs)¶
Large Language Models are foundation models trained on vast amounts of text data to understand and generate human-like language. They range from smaller, specialized models (7B parameters) to massive general-purpose models (100B+ parameters). LLMs power most modern AI applications through text generation, analysis, reasoning, and transformation capabilities.
When to use: Text generation, analysis, chatbots, code assistance, translation, summarizationâthe foundation of most AI applications.
AI Agents¶
AI agents are LLM-powered systems that can use tools, make decisions, and execute multi-step workflows autonomously. Unlike simple chatbots, agents can plan actions, call external APIs, and adapt based on results. Common patterns include ReAct (reasoning + acting) and multi-agent systems where specialized agents collaborate on complex tasks.
When to use: Automating workflows, research tasks, customer support, code generation with tool access.
Function Calling & Tool Use¶
Function calling enables LLMs to interact with external systems by invoking structured functions or APIs. The model decides when to call a function, extracts the necessary parameters from natural language, and interprets the results. This bridges LLMs with real-world actions like database queries, API calls, or calculations.
When to use: AI agents, chatbots needing real-time data, automation tasks, integrating AI with existing systems.
Retrieval-Augmented Generation (RAG)¶
RAG combines LLMs with external knowledge sources by retrieving relevant information before generating responses. Instead of relying solely on training data, RAG systems search through documents, databases, or knowledge bases to provide current, accurate information. Key components include vector databases, embeddings, and retrieval strategies.
When to use: Question answering over documents, knowledge bases, chatbots needing current information.
Model Context Protocol (MCP)¶
MCP is an emerging standard for connecting AI models with tools and data sources. It provides a consistent way for models to discover, invoke, and compose capabilities across different systems, reducing integration complexity and enabling shared tooling across the AI ecosystem.
When to use: Building interoperable AI tools, standardizing agent-tool communication, future-proofing integrations.
Voice AI¶
Real-time voice AI enables natural spoken conversations with minimal latency. Modern voice agents combine speech recognition, language models, and text-to-speech into seamless interactions accessible via phone, apps, or devices. Technologies like OpenAI's Realtime API and platforms like Twilio and Vapi make voice integration increasingly accessible.
When to use: Phone assistants, voice interfaces, accessibility features, hands-free applications.
Prompt Engineering¶
Prompt engineering is the practice of crafting effective instructions for LLMs to achieve desired outputs. It includes techniques like few-shot learning, chain-of-thought reasoning, and structured output formatting. Well-designed prompts significantly impact response quality, consistency, and reliability.
When to use: All LLM applicationsâit's fundamental to getting good results from any AI system.
Fine-tuning¶
Fine-tuning adapts pre-trained models to specific tasks or domains by training on custom datasets. Unlike prompt engineering, fine-tuning modifies the model's weights to specialize its behavior, improve accuracy for specific use cases, or reduce costs by using smaller models. It requires labeled training data and computational resources but can significantly improve performance for specialized applications.
When to use: Domain-specific tasks, improving accuracy beyond prompting, reducing token usage, custom tone/style.
Multimodal AI¶
Multimodal AI processes and generates multiple types of dataâtext, images, audio, and videoâin combination. Modern multimodal models can understand images and respond with text, generate images from descriptions, or analyze videos. This enables richer interactions and applications that work with the full spectrum of human communication.
When to use: Image analysis, visual Q&A, document processing with diagrams, content generation, accessibility tools.
Vector Databases¶
Vector databases store and search high-dimensional embeddings (numerical representations of text, images, or other data). They enable semantic search where queries find conceptually similar content rather than just keyword matches. Popular options include Pinecone, Chroma, Qdrant, and Weaviate.
When to use: RAG systems, semantic search, recommendation engines, content similarity matching.
Evaluations¶
LLM evaluations measure model performance, accuracy, and quality. This includes automated testing (evals), human review, and metrics like relevance, correctness, and hallucination rates. Robust evaluation frameworks help ensure AI systems perform reliably in production.
When to use: Testing model changes, validating RAG accuracy, monitoring production quality, compliance.
Guardrails¶
Guardrails are safety mechanisms that constrain AI behavior and outputs. They include input validation, content filtering, output monitoring, and rules to prevent harmful, biased, or inappropriate responses. Guardrails can be implemented at the prompt level, through external validation, or via specialized frameworks.
When to use: Production AI systems, customer-facing applications, compliance requirements, risk mitigation.
Security¶
AI security encompasses protecting models from attacks (prompt injection, jailbreaking), securing API keys and data, implementing access controls, and ensuring data privacy. Security also includes rate limiting, cost controls, and monitoring for misuse or anomalous behavior.
When to use: All production AI systemsâsecurity should be a foundational consideration, not an afterthought.
Ethics & Responsible AI¶
Ethical AI development addresses fairness, bias, transparency, accountability, and societal impact. This includes considering how models affect different populations, being transparent about AI limitations, providing explanations for decisions, and designing systems that align with human values.
When to use: Alwaysâespecially in applications affecting people's opportunities, safety, or wellbeing.