Tag: ai-concepts
All the articles with the tag "ai-concepts".
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What to learn next — your AI engineering learning path after this course
You have built a production-ready AI application from scratch. You understand embeddings, RAG, agents, memory, and how to ship safely. This post maps where to go from here — deeper specialisations, adjacent skills, and the emerging areas worth watching.
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LangChain4j vs Spring AI — which Java AI framework should you use?
Two mature Java AI frameworks exist — Spring AI and LangChain4j. They solve the same problems with different philosophies. This post maps their concepts side-by-side, compares their strengths, and offers a clear decision guide for new projects.
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AI agent patterns — when to use simple chains, RAG, or full agents
Not every AI feature needs an agent. This post maps the decision: when a single LLM call is enough, when a prompt chain is better, when RAG solves it, and when you actually need a multi-step agent. Includes reliability considerations and a decision framework.
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What is an AI agent? Moving beyond single LLM calls
A single LLM call answers a question. An AI agent reasons, decides which tools to use, calls them, observes results, and loops until the task is complete. This post explains the concept clearly and when you actually need an agent vs a simpler approach.
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Managing context window efficiently — windowed memory and summarization
Sending full conversation history on every request is expensive and eventually hits the context window limit. Windowed memory keeps only recent turns. Summarization condenses older history into a compact summary. This post shows both techniques and when to use each.