@bashifuirkashi: 1️⃣ RAG vs Fine-Tuning RAG gives the model external information at runtime. Fine-tuning changes the model’s weights and behavior. 2️⃣ Vector Database vs Knowledge Graph Vector databases retrieve based on semantic similarity. Knowledge graphs model relationships between entities and let you traverse those connections. 3️⃣ AI Agent vs AI Workflow Workflows follow mostly predetermined steps. Agents can decide what action to take, which tools to use, and what to do next. 4️⃣ Context vs Memory Context is what the model has available for the current request. Memory lets the system store and retrieve information from previous interactions. 5️⃣ Retrieval vs Re-Ranking Retrieval finds potentially relevant documents. Re-ranking scores those results again to decide what should actually be passed to the LLM. 6️⃣ Logging vs Tracing Logging records individual events. Tracing follows the entire request across model calls, tools, retrieval, and agents so you can understand exactly what happened. 7️⃣ Software Engineering vs AI Engineering AI engineers still need strong software engineering fundamentals like code, APIs, databases, and infrastructure, but they also need to know how to integrate models, evaluate AI systems, and make unpredictable AI components reliable enough for production. If you’re trying to break into AI engineering, comment “DIFFERENCE” and I’ll send you the link to my AI engineering community with a full learning roadmap + daily calls with AI/ML engineers.

Bashi | Software Engineer

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Thursday 27 August 2026 16:00:23 GMT