AI Must-Know Terms for Langchain Developers [2025]
If you’re diving into AI development, or experimenting with frameworks like LangChain, this guide will walk you through the essential terms every developer should know.
Tin Martinić
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Let's start from the basics - Artificial Intelligence (AI) is the field of computer science focused on building machines that can mimic human intelligence, not just by following fixed instructions, but by learning, reasoning, and adapting.
Branches of AI
Machine Learning (ML) - Machine learning systems learn from data identifying patterns and improving their predictions over time without explicit reprogramming.
Deep Learning - A subset of machine learning that uses neural networks with multiple layers (“deep”) to process complex data such as images, sound, or text. Deep learning powers speech recognition, facial detection and LLMs.
Natural Language Processing (NLP) - This branch is built on Deep Learning and focuses on enabling computers to understand and generate human language. Used in translating texts, detecting emotions, summarizing documents and chatbots and voice assistants.
Computer Vision - Deep learning made this field explode in accuracy, using convolutional neural networks (CNNs). It is used for detecting faces in photos, self-driving car vision systems and medical image analysis.
Generative AI - Generative AI refers to systems that don’t just analyze data, they generate new content. Such as text, images, audio, video, code
Large Language Models (LLMs)
Large Language Models (LLMs) are AI models trained to understand and generate human-like text. They’re the backbone of modern NLP systems, powering chatbots, summarizers, translators, and even code generators.
At their core, LLMs predict the next token in a sequence based on the context provided in a prompt.
Key concepts of LLMs
Prompt - The input text you give the model that sets context and instructs it what to do. This is where you “tell” the model what task to perform.
Tokens - The smallest units of text the model processes (words or subwords).
Context Window - Maximum number of tokens the model can “see” at once; includes both the prompt and generated text.
Temperature - Controls randomness in output: 0 = deterministic, 1 = creative.
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Tin Martinić
Neviox Digital CEO with an Engineer's degree in Computer Science from the University of Split, and multiple certifications, my core competencies span across front-end and back-end technologies, with an emphasis on Langchain, Next.js and AWS
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Embeddings - Numerical representations of text, used for semantic search, similarity, or retrieval.
Currently most popular LLMs are OpenAI, Anthropic (Claude), Mistral, Llama and Gemini
AI Tools
When building AI-powered apps, it’s not just about calling a model API. Developers rely on a mix of frameworks, libraries, and platforms to manage prompts, chains, data, and deployment.
LLM APIs - These are services that let your app communicate with LLMs without training them yourself.
AI Development Frameworks (Langchain, LlamaIndex) - These frameworks help you organize logic, manage prompts, and chain multiple steps together with potential of using your own data for more specified output.
Vector Databases - Store embeddings for semantic search and RAG pipelines
Document Loaders & Parsers - Read PDFs, CSVs, websites and other formats into your pipeline
Prompt Tools ( Langsmith, PromptLayer) - Prompt engineering is critical, these tools help manage prompts and pipelines.
AI Memory
Memory is what allows LLM-powered systems to maintain continuity and context, enabling multi-step reasoning, personalized interactions, and RAG pipelines. Chains, agents, and tools all rely on memory to be truly useful beyond a single prompt.
Short-term Memory - Session memory that keep track of current conversation or chain execution implemented by passing previous message into the prompt context.
Long-term Memory - Stores the memory across the sessions or over time using database, vector store or a file.
LangChain & Modern LLM Workflows with Your Own Data
LangChain isn’t just about calling an LLM. Its real power lies in making LLMs work with your data or your clients’ data, enabling developers to build accurate, context-aware, and actionable AI applications.
Think of it as a framework for structured AI workflows: it lets you combine chains, memory, reasoning, retrieval, and tools, all centered around your data.
Retrieval Augmented Generation (RAG)
Core feature for working on your own data.Instead of relying solely on the model’s knowledge, RAG retrieves relevant documents from your data and feeds them into the LLM. Use case: A real estate assistant bot can answer questions about properties by first fetching the specific listings from a vector database.
Agents
Agents go a step beyond chains - they decide which actions or tools to use dynamically, based on the context or retrieved data. Use case: An agent can query multiple knowledge bases, run calculations, and generate a final response — all automatically
Reflection Agents - Let the agent think about its past actions and improve future decisions. This helps in long-running tasks where cumulative knowledge or experience improves performance.
Reflexion Agents - Reflexion is an architecture where an agent self-critiques its own outputs using verbal feedback, grounding its evaluation in external data. By generating citations and explicitly identifying missing or superfluous parts, it produces constructive reflections that guide the agent to improve future responses.
Agentic RAG
Combines the strengths of RAG and agents.The LLM can retrieve external knowledge, reason over it, and decide what steps to take.
Use case: A specific research assistant that pulls internal reports, summarizes findings, and decides follow-up actions.
Prompt Engineering
Critical when working with client data, prompt engineering ensures the model understands how to use retrieved information and context. Mastering LLM prompting can significantly reduce operating costs and improve output quality.
Tips: Provide structured examples, include relevant memory, and guide the model toward predictable outputs.
LangGraph
Visual workflow tool that helps design and debug chains, agents, and memory flows.
Especially useful when building complex, multi-step, data-driven applications.
LangChain transforms an LLM from a generic text generator into a powerful, data-aware AI assistant. By combining chains, memory, RAG, and agents, you can build applications that understand, reason, and act using your own or client-specific data which is the real advantage of using LangChain in production.
Langchain Applications in Production
Building AI applications with LangChain is exciting, but deploying them in the real world requires attention to performance, cost, safety, and maintainability. Here are the most critical factors developers should watch out for:
Rate Limiting - Enforce API call limits per minute or month and avoid repeated LLM calls if possible using prompt caching or vector stores.
Observability - Use logging, dashboards, or tools like PromptLayer or LangSmith to debug workflows and spot bottlenecks or errors.
LLMOps - Handle scaling, retries, and failovers, monitor latency and token consumption, maintain versioning of prompts, chains, and memory structures
Guardrails & Safety - Prevent unsafe or inappropriate outputs by adding filters, content moderation, or validation steps.
Benchmarking - Continuously measure accuracy, reasoning quality, and relevance. Compare models, prompts, and chains to find the best configuration for your application.
Use smaller models where possible, and apply retrieval and RAG techniques to limit the amount of text sent to the LLM.
Deploying LangChain apps isn’t just about functionality, it’s about balancing cost, performance, and safety while ensuring maintainable and observable workflows. Planning for these aspects upfront saves time and reduces risks in production environments.
Interesting Topic - MCP
MCP stands for Model Control Plane (sometimes called “Managed Control Plane” in certain vendor docs). It’s an abstraction layer that sits between your application and LLM models, providing features that go beyond just sending API calls.
Key Features of MCP
Routing & orchestration: Decide which model or instance to use dynamically.
Load balancing: Manage multiple LLM endpoints to handle high traffic.
Monitoring & observability: Track usage, latency, token consumption, and errors.
Versioning & configuration: Control which model version or prompt template is used per workflow.
Security & governance: Apply guardrails, access control, and policy enforcement.
MCP vs API
A Direct API is the simplest way to interact with an LLM: your application sends a request, the model generates a response, and that’s it. This approach works well for prototypes, small projects, or low-traffic applications because it’s straightforward and requires minimal infrastructure.
A Model Control Plane (MCP), on the other hand, adds a layer of orchestration and management between your app and the LLMs. It handles routing requests to the appropriate model, balancing load across multiple instances, monitoring usage and performance, managing versions, and applying security or guardrails. Essentially, it turns raw API calls into a scalable, observable, and production-ready workflow.
In practice: use a Direct API for experimentation or simple apps, and an MCP when building multi-client, production-grade systems where you need efficiency, observability, and governance over your LLM workflows.
