When you're building a GenAI application, especially in a production-ready stack like Next.js + NestJS, choosing between RAG, fine-tuning, and prompt engineering depends on your use case, data availability, cost tolerance, and desired performance. Here’s a clear breakdown of each method, when to use it, and how to optimize it for your stack.

🔍 Quick Summary

Approach Use Case Pros Cons
Prompt Engineering Fast iteration, small customizations No infra cost, quick to implement Limited by model’s existing knowledge
RAG (Retrieval-Augmented Generation) Domain-specific knowledge injection Keeps LLM fresh, cheap compared to fine-tuning Needs retrieval pipeline and vector DB
Fine-Tuning Repetitive, predictable domain tasks Deep model alignment with your domain Expensive, time-consuming, risk of model drift

🧠 Prompt Engineering

When to Use

  • You need fast results without heavy infra setup.
  • The base model already knows a lot, and you just want better formatting, tone, or clarity.

Key Practices

  • Use instructional prompts: "You are a QA assistant. Read the following spec and generate BDD-style test cases."
  • Apply few-shot examples: Show input-output pairs to guide the model.
  • In Next.js/NestJS:
    • Maintain prompt templates in files or a headless CMS.
    • Load and customize them server-side before hitting OpenAI APIs.

📚 Retrieval-Augmented Generation (RAG)

When to Use

  • Your data is proprietary, frequently changing, or not part of public LLM knowledge.
  • You want to inject context at runtime without retraining.

Core RAG Flow

  1. Document ingestion: Parse + chunk specs/test cases (NestJS service).
  2. Embedding: Use @nestjs/axios or openai SDK to get vector embeddings.
  3. Vector Store: Store embeddings in MongoDB Atlas Vector Search or similar.
  4. Context Assembly: On query, retrieve top-k relevant docs, add to prompt.
  5. Generate: Send to LLM with retrieval context.

Optimization Tips

  • Use semantic chunking (headings, bullets, etc.) for better retrieval.
  • Rank documents using cosine similarity + metadata filters.
  • Cache recent vector results in Redis for repeat queries.

🧬 Fine-Tuning

When to Use

  • Your task is narrow, repetitive, and needs domain-specific phrasing or labels.
  • You're building something like:
    • Auto-generating Jira test cases,
    • Classifying support tickets,
    • Labeling logs with internal codes.

Workflow

  • Prepare structured JSONL training data.
  • Use OpenAI, HuggingFace, or other platforms to fine-tune a base model.
  • Host model (optionally) using Azure OpenAI or a local inference engine.

In Your Stack

  • Upload datasets from your Next.js admin panel.
  • Use a NestJS queue (BullMQ) to process and send fine-tune jobs.
  • Version your models and choose them via API route (e.g., POST /generate?model=v2).

🚀 Which Should You Choose?

Scenario Recommendation
Your app updates specs every sprint RAG
You want faster response without re-embedding Prompt Engineering
Your test case format is repetitive and domain-locked Fine-Tuning
You want full control over app behavior RAG + Prompt Engineering

🧱 Stack Implementation Tips (Next.js + NestJS)

NestJS

  • RAG: Use services for chunking, embedding, and vector search.
  • Use @nestjs/schedule or BullMQ for background processing.
  • Create a PromptBuilderService that composes prompts based on context.

Next.js

  • Stream outputs using React Server Actions + ReadableStream (for OpenAI streaming).
  • Upload docs with a dropzone (e.g., react-dropzone) and send to NestJS API.
  • Use SWR or TRPC for query caching and UI sync.

🔄 Combine Them

The best GenAI apps combine prompt engineering + RAG, and evolve into fine-tuning when data is mature.

Example:

Use prompt engineering for base structure → Use RAG to enrich with domain context → Fine-tune later to reduce latency/costs.