If you’re a Product Manager working with Large Language Models (LLMs), here’s a reality check: you don’t need to be a data scientist, but you do need to understand the methods that make AI products shine. They are the building blocks behind any scalable, impactful AI product using . Think of them as the PM’s cheat sheet to unlocking AI potential.
Here’s the shortlist of essential LLM methods. Each one can make or break the success of your AI product:
- RAG (Retrieval-Augmented Generation) When accuracy matters (and let’s be honest, it always does), RAG connects your LLM to trusted data sources. This means fewer hallucinations and more grounded answers. Imagine your chatbot pulling verified responses straight from your product documentation.
- RLHF (Reinforcement Learning from Human Feedback) Think of this as teaching your AI to “read the room.” RLHF aligns AI outputs with user preferences, ethical guidelines, and practical needs. Without it, you risk building a tool that doesn’t “get” your users.
- Fine-Tuning (FT) Fine-tuning transforms a generalist model into a domain expert. Need your AI to speak healthcare, legal, or financial jargon fluently? Fine-tuning is your go-to method for creating industry-specific solutions.
- PEFT (Parameter-Efficient Fine-Tuning) Here’s the leaner version of fine-tuning. PEFT helps you adapt open-source models to your needs without burning through your budget. Perfect for startups or teams scaling AI features on a tight resource plan.
- LoRA (Low-Rank Adaptation) LoRA is the customization hack for teams on the move. It focuses on fine-tuning smaller, critical parts of the model, meaning you get big results with minimal computing power. If you’re working with Llama 2 or similar, this should be on your radar.
- MoE (Mixture of Experts) Scaling user demands? Meet MoE. It routes tasks to specialized sub-models, much like assigning the right job to the right expert on your team. Efficient, effective, and scalable.
- Active Learning (AL) Why train your AI on all the data when only some of it matters? AL identifies the most impactful training data, speeding up improvements while cutting costs. Think of it as your data strategy whisperer.
These methods are strategic and will give you levers to pull when you’re:
- Working on a budget: Use PEFT or LoRA for cost-efficient fine-tuning.
- Building user trust: Implement RAG to deliver reliable, fact-based outputs.
- Scaling your product: Explore MoE for handling diverse user demands.
- Optimizing your data: Turn to AL for smarter, leaner data collection.
- Aligning with user needs: Leverage RLHF to make AI outputs intuitive and relevant.
The beauty of these methods lies in their versatility. Here’s how you can match them to your product challenges, you can use...
- RAG when accuracy is critical (hint: it’s always critical).
- LoRA or PEFT when your compute or budget is tight.
- Fine-Tuning when you need deep domain expertise.
- Active Learning when building a smarter data strategy.
- MoE for efficient scaling.
Each method opens the door to new possibilities for your AI products. The trick is knowing which one to use, when, and how. These methods are part of the toolkit that has helped me create smarter, scalable, and impactful AI solutions. As PMs, our role is to bridge user needs with what’s technically possible, and this knowledge makes all the difference.
