[Verse 1] Sarah's got a chatbot that won't understand her customers Generic responses driving business down the gutter She needs AI that speaks her company's language Three paths diverge in the customization passage [Chorus] Prompting's quick and dirty, just massage the input text RAG retrieves your knowledge when context comes up next Fine-tuning rewrites neurons but costs the most to train Pick your poison wisely, each has different gain Quick or deep or hybrid, match your budget to your pain [Verse 2] Prompting costs you pennies, just craft clever instructions "Act like you're a lawyer" drives model's deductions But hallucinations creep in when knowledge runs too thin Perfect for prototyping when you're just diving in [Chorus] Prompting's quick and dirty, just massage the input text RAG retrieves your knowledge when context comes up next Fine-tuning rewrites neurons but costs the most to train Pick your poison wisely, each has different gain Quick or deep or hybrid, match your budget to your pain [Verse 3] RAG builds a database, searches through your documents Injects relevant chunks before the model comments Keeps facts accurate while maintaining speed Perfect middle ground for most business need [Bridge] Fine-tuning burns through GPU hours Reshapes the model's hidden powers Specialized behavior, permanent change But expensive cycles, narrow range [Verse 4] Start with prompts to test your concept fast Add RAG when accuracy must last Fine-tune only when you've proven the case And budget allows for that computational race [Chorus] Prompting's quick and dirty, just massage the input text RAG retrieves your knowledge when context comes up next Fine-tuning rewrites neurons but costs the most to train Pick your poison wisely, each has different gain Quick or deep or hybrid, match your budget to your pain [Outro] Sarah chose RAG and her customers smile Accurate responses with conversational style Three tools in your arsenal, know when each applies The CTO's wisdom helps your project fly
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