RAGs, Fine tuning or just Prompt Engineering, what's the right choice? ft. Afaq Ahmad | RipeSeed скачать в хорошем качестве

RAGs, Fine tuning or just Prompt Engineering, what's the right choice? ft. Afaq Ahmad | RipeSeed

Everyone wants to build something with AI today, a chatbot, a voice agent, an internal assistant, or a customer support bot. But once you start, you hit a confusing question: Should I use prompt engineering? RAG? Or fine-tune a model? Here’s the truth: These 3 approaches are NOT competing with each other. They solve different problems, and choosing the wrong one early (usually due to hype/peer pressure) is one of the biggest mistakes teams make. In this video, I break down exactly when to use Prompt Engineering vs RAG vs Fine-Tuning, with real examples, so you can choose the right approach and avoid unnecessary complexity. What you’ll learn in this video: Why prompt engineering should always be your starting point When RAG (Retrieval-Augmented Generation) becomes necessary What fine-tuning is actually for (and when it’s not worth it) A simple decision flow to avoid overengineering AI products Real-world examples of each approach Prompt Engineering (start here, no exceptions) Prompt engineering means telling the model how to behave using instructions and examples. You can also add FAQs and common Q&A inside the prompt. This alone solves more problems than people expect. Works best when: Your logic is simple Your knowledge is limited Your behavior rules are clear Examples: Website chatbot answering common questions Lead qualification bot asking fixed questions RAG (when your knowledge is too big for a prompt) If your model needs more context than you can fit in a prompt, use RAG. Instead of putting everything inside the prompt, you fetch only the relevant information at runtime and pass it to the model as context. Works best when: Your data is large Your data changes often Accuracy matters Examples: Customer support bots answering from docs Internal tools reading PDFs or Notion pages Policy or compliance assistants Fine-Tuning (for consistent behavior and strict output formats) If prompt engineering + RAG still doesn’t solve the problem, and you need consistent behavior/output patterns, fine-tuning comes in. Fine-tuning is mainly for controlling behavior and response style at scale, and reducing randomness in outputs. Examples: Medical reports with strict formats Legal drafts with consistent clauses Important: Fine-tuning is expensive, harder to debug, slower to iterate — so think twice before doing it. Recommended flow: Start with prompt engineering. Add RAG if you need external knowledge that’s large or frequently changing. Go for fine-tuning only if you need strict and consistent behavior/output patterns. Because at the end of the day: You don’t get bonus points for adding complexity. Users only care about results — and simpler approaches often achieve better outcomes. If you’re building AI chatbots, voice agents, or internal assistants and want help picking the right architecture (Prompt vs RAG vs Fine-Tuning), feel free to reach out. #AI #Chatbots #RAG #PromptEngineering #FineTuning #LLM #AIAgents #VoiceAgents #MachineLearning #AIStartup