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Low-Effort LLM Tuning and RAG
Created by Anirudh Shrikanth
Learn how modern teams adapt large language models without training from scratch. You’ll practice deciding when to use fine-tuning vs RAG, understand parameter-efficient tuning and quantization at a mechanical level, and finish with evaluation and alignment workflows used in real systems.
Requirements
- What an LLM/chatbot is and how prompts affect outputs
- High-level idea of training vs inference (weights are learned numbers)
- Basic comfort with compute/memory tradeoffs (VRAM/RAM as constraints)
What you'll learn
- Choose between RAG and fine-tuning for a given product requirement and justify the choice using concrete criteria.
- Explain, in practical terms, what gets updated in parameter-efficient tuning and why this reduces training cost and hardware requirements.
- Describe what quantization changes (numeric representation and storage) and how that impacts memory and quality tradeoffs.
- Sketch an end-to-end RAG architecture including embeddings, a vector database, retrieval, and grounded generation.
- Use evaluation thinking to diagnose whether failures are primarily retrieval-related or behavior/alignment-related, and select the next improvement step accordingly.
Learning path
6 modules • Each builds on the previous one
1
Fine-tuning vs instruction tuning basics
Clarify what “fine-tuning” changes in a pretrained LLM, and how instruction tuning differs from prompt engineering (it updates weights to follow instructions reliably). You’ll map common goals (style, tool use, domain language) to the right tuning stage.
1 video9 min
2
Instruction tuning dataset design essentials
Learn how to design instruction-following datasets: prompt/response schemas, diversity, difficulty balancing, deduplication, safety filtering, and train/validation splits. Focus on what actually improves behavior (coverage, consistency, and high-signal examples).
1 video8 min
3
PEFT and frozen weights explained
Understand parameter-efficient fine-tuning (PEFT): the base model weights stay frozen, while small modules learn task-specific updates. Connect this to why PEFT is cheaper and often safer than full fine-tuning.
1 video9 min
4
Precision, quantization, and QLoRA 4-bit
Fix the quantization misconception: quantization changes how weights are stored (e.g., 16-bit → 4-bit), while some computations and adapters often remain higher precision. Then connect this to QLoRA: a 4-bit quantized base model plus trainable LoRA adapters for low-VRAM fine-tuning.
1 video9 min
5
RAG vs fine-tuning with vector databases
Compare RAG vs fine-tuning trade-offs (freshness, cost, controllability, latency), then learn the core components of RAG: embeddings, chunking, vector indexes, retrieval, and reranking. You’ll understand how a vector database fits into an end-to-end RAG pipeline.
1 video10 min
6
Model evaluation, perplexity, RLHF, DPO
Learn how to evaluate fine-tuned models (perplexity, task metrics, preference/win-rate eval) and how alignment methods build on that: RLHF (reward modeling + policy optimization) versus DPO (a simpler, modern preference-optimization approach). Emphasis is on practical selection and “low effort” implementation paths.
2 videos14 min
Start Learning
Begin your learning journey
Modules6
Duration56 min
Science-backed learning
In-video quizzes and scaffolded content to maximize retention.
Key concepts
RAG Vs Fine-Tuning Decision Criteria (Freshness, Cost, Controllability)Instruction-Following Specialization Workflows (Low-Effort Tuning Paths)Parameter-Efficient Tuning Intuition (Small Trained Modules Vs Full Updates)Frozen Base Weights Vs Trainable Add-Ons (What Changes During Efficient Tuning)Quantization As Precision Reduction (Q8/Q4/Q2 Tradeoffs)Vector Embeddings And Similarity Search For RetrievalVector Databases (Indexing And Fast Semantic Search) In RAG PipelinesEvaluating RAG Systems With Retrieval-Focused MetricsRLHF Pipeline For Aligning Model Behavior To Human Preferences