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( 10 Students )
( 10 Students )
Fine-Tuning and RAG
Master fine-tuning of LLMs and build advanced Retrieval-Augmented Generation (RAG) systems for high-performance, domain-specific AI solutions.
Course Duration: 10 Hours
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The ability to customize large language models (LLMs) and provide them with domain-specific knowledge is revolutionizing the way AI applications are built. This self-paced, hands-on course is designed for AI developers, ML engineers, NLP practitioners, and enterprise architects looking to gain end-to-end proficiency in Fine-Tuning and Retrieval-Augmented Generation (RAG).
Fine-tuning involves updating a pre-trained model’s weights to adapt it to a specific task or dataset. This is crucial when domain knowledge, task-specific understanding, or unique outputs are required that general models cannot provide. In contrast, RAG enables LLMs to access external data sources like vector databases at inference time—bringing in relevant context without retraining.
This course teaches how to fine-tune foundational models like BERT, GPT-2/3, Falcon, and LLaMA, and how to architect scalable RAG pipelines using tools like HuggingFace, LangChain, FAISS, and ChromaDB. Whether your goal is building legal assistants, personalized chatbots, or internal knowledge systems, this course gives you both theory and practice.
What Are Fine-Tuning and RAG?
- Fine-tuning modifies a base LLM to improve its performance on a specific domain or task using labeled data.
- RAG (Retrieval-Augmented Generation) combines LLMs with external data sources, pulling relevant information at inference time to generate more accurate and grounded responses.
Together, they form a powerful strategy—fine-tune when general models are insufficient, and use RAG when dynamic, scalable knowledge access is needed.
How to Use This Course
- Start with Foundations – Understand transformers, embeddings, and the pretraining-finetuning paradigm.
- Train on Your Data – Learn how to format datasets, tokenize inputs, and run fine-tuning on GPU using HuggingFace Trainer.
- Apply RAG Techniques – Build semantic search systems and integrate them with LLM prompts using LangChain or LlamaIndex.
- Compare Approaches – When to fine-tune vs. when to retrieve? Learn trade-offs in cost, latency, and performance.
- Capstone Projects – Apply both techniques to production apps: personalized legal bot, domain Q&A assistant, or multi-language tutor.
The course blends conceptual clarity with real-world development workflows, ensuring you can build, evaluate, and scale high-performance LLM systems.
Course/Topic 1 - Coming Soon
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The videos for this course are being recorded freshly and should be available in a few days. Please contact info@uplatz.com to know the exact date of the release of this course.
Course Objectives Back to Top
By the end of this course, learners will be able to:
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Understand when and how to apply fine-tuning and RAG techniques.
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Prepare and preprocess datasets for fine-tuning transformer-based models.
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Fine-tune models like BERT, DistilBERT, GPT-2, or LLaMA using HuggingFace.
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Use LoRA, PEFT, and QLoRA for parameter-efficient fine-tuning.
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Generate and store embeddings in vector databases (FAISS, Pinecone).
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Implement Retrieval-Augmented Generation pipelines using LangChain or LlamaIndex.
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Evaluate models using metrics like perplexity, BLEU, and grounding scores.
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Build hybrid architectures combining fine-tuning and retrieval.
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Deploy fine-tuned models and RAG pipelines using FastAPI, Streamlit, or Docker.
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Optimize performance and latency using caching, batching, and quantization.
Course Syllabus Back to Top
Course Syllabus
Module 1: Intro to Fine-Tuning and RAG
- LLM evolution: Pretraining vs. fine-tuning
- What is RAG and why does it matter?
- Comparing prompt engineering, fine-tuning, and retrieval
Module 2: Transformer Architecture & Tokenization
- Positional encoding, attention, and output generation
- Tokenization types and dataset formatting
- Intro to HuggingFace Transformers
Module 3: Dataset Preparation for Fine-Tuning
- Collecting, cleaning, and formatting datasets
- Creating instruction-following datasets
- Loading with HuggingFace Datasets and DataLoaders
Module 4: Fine-Tuning in Practice
- Training custom BERT or GPT-2 models
- Using HuggingFace Trainer API
- Evaluation metrics and early stopping
Module 5: Parameter-Efficient Fine-Tuning (PEFT)
- Introduction to LoRA, QLoRA, and adapters
- Fine-tuning large models under resource constraints
- Using bitsandbytes, PEFT library, and DeepSpeed
Module 6: Introduction to Embeddings and Vector Stores
- Embedding generation with OpenAI, Cohere, HuggingFace
- FAISS, ChromaDB, Pinecone basics
- Indexing, searching, and filtering vectors
Module 7: RAG Pipeline Architecture
- Chunking documents, generating embeddings
- Building retrieval logic
- Connecting retriever with LLM for RAG
Module 8: RAG with LangChain & LlamaIndex
- LangChain retriever chains and vector memory
- Hybrid search and metadata filtering
- Prompt templates and document compression
Module 9: Hybrid Architectures
- Combining fine-tuned models with retrieval systems
- Trade-offs between accuracy, cost, and latency
- Use cases: Legal, enterprise, e-commerce bots
Module 10: Deployment and Optimization
- Serving models using FastAPI, Streamlit, or Gradio
- GPU vs. CPU optimization, caching, batching
- Monitoring model quality and retrieval success
Module 11: Capstone Projects
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Fine-tune vs. RAG decision matrix
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Build a scalable chatbot with dynamic context
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Serve a personalized Q&A system with memory
Certification Back to Top
Upon completion of the course, learners will receive a Certificate of Completion from Uplatz, verifying their skills in both fine-tuning LLMs and implementing Retrieval-Augmented Generation systems. This certificate reflects your ability to customize foundation models, optimize them for specific tasks, and combine them with dynamic, external knowledge sources for high-impact AI solutions. Employers and clients value professionals who can strike the right balance between performance, cost-efficiency, and accuracy—especially in regulated or specialized domains. Whether you're building customer support bots, internal knowledge systems, or intelligent R&D assistants, this certification shows you have the hands-on knowledge and architectural thinking required to succeed. The certificate can be added to your resume, LinkedIn profile, or GitHub portfolio and is ideal for engineers, AI developers, ML researchers, and technical leads working with generative AI.
Career & Jobs Back to Top
As organizations embrace large language models, they require customization, control, and contextual accuracy—which is where fine-tuning and RAG come in. These techniques are becoming central to modern enterprise AI solutions, powering intelligent assistants, legal advisors, customer service bots, compliance tools, and search systems.
Job titles where these skills are critical include:
- LLM Engineer
- NLP Engineer
- Applied AI Researcher
- Prompt Optimization Specialist
- Conversational AI Architect
- AI Platform Engineer
- Data Scientist (NLP focus)
Fine-tuning allows teams to train domain-specific models, while RAG enables models to retrieve current and accurate data at runtime. These two strategies are now essential pillars of AI product development, especially where accuracy and safety are critical.
Companies across healthcare, finance, legal, education, and tech are hiring developers who can work with HuggingFace, LangChain, vector databases, and fine-tuned transformer models. By learning to apply LoRA and PEFT techniques, you reduce training costs and latency—skills highly sought after in startups and scaled AI deployments alike.
Freelancers can offer services like custom chatbot development, legal research assistants, or multilingual tutoring apps powered by RAG pipelines and fine-tuned LLMs. With the rise of open-source models (Mistral, LLaMA, Falcon) and vector-native architectures, this course places you at the forefront of applied generative AI.
Interview Questions Back to Top
1. What is the difference between fine-tuning and prompt engineering?
Prompt engineering crafts better prompts for pre-trained models, while fine-tuning changes the model weights to adapt to new tasks or data.
Prompt engineering crafts better prompts for pre-trained models, while fine-tuning changes the model weights to adapt to new tasks or data.
2. What is parameter-efficient fine-tuning (PEFT)?
PEFT techniques like LoRA or adapters allow updating only a small part of the model, making fine-tuning efficient on limited resources.
PEFT techniques like LoRA or adapters allow updating only a small part of the model, making fine-tuning efficient on limited resources.
3. When should you use RAG over fine-tuning?
Use RAG when knowledge changes frequently or when responses require dynamic data access. Fine-tuning is best when behavior must be learned or frozen.
Use RAG when knowledge changes frequently or when responses require dynamic data access. Fine-tuning is best when behavior must be learned or frozen.
4. How does LoRA work?
LoRA introduces trainable matrices into the model and freezes original weights, reducing memory usage and allowing efficient fine-tuning.
LoRA introduces trainable matrices into the model and freezes original weights, reducing memory usage and allowing efficient fine-tuning.
5. What is the role of vector databases in RAG?
They store embeddings and allow similarity-based search to retrieve relevant chunks for LLMs during inference.
They store embeddings and allow similarity-based search to retrieve relevant chunks for LLMs during inference.
6. What are common challenges in fine-tuning LLMs?
Overfitting, catastrophic forgetting, high compute costs, and data formatting are common hurdles.
Overfitting, catastrophic forgetting, high compute costs, and data formatting are common hurdles.
7. What is retrieval latency and how can it be optimized?
Retrieval latency is the time to fetch data from a vector store. Optimize using faster index types, preloading, and caching.
Retrieval latency is the time to fetch data from a vector store. Optimize using faster index types, preloading, and caching.
8. Can you combine fine-tuning and RAG in one system?
Yes. Fine-tune for behavior or tone, and use RAG for dynamic knowledge injection—ideal for grounded, personalized bots.
Yes. Fine-tune for behavior or tone, and use RAG for dynamic knowledge injection—ideal for grounded, personalized bots.
9. What tools are used for fine-tuning transformers?
HuggingFace Transformers, Datasets, PEFT, Accelerate, bitsandbytes, and DeepSpeed are common tools.
HuggingFace Transformers, Datasets, PEFT, Accelerate, bitsandbytes, and DeepSpeed are common tools.
10. How do you evaluate the performance of a RAG system?
Using grounding accuracy, BLEU/ROUGE scores, retrieval relevance, hallucination rate, and latency benchmarks.
Using grounding accuracy, BLEU/ROUGE scores, retrieval relevance, hallucination rate, and latency benchmarks.
Course Quiz Back to Top
FAQs
Back to Top
Q1. What are the payment options?
A1. We have multiple payment options:
1) Book your course on our webiste by clicking on Buy this course button on top right of this course page
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Q2. Will I get certificate?
A2. Yes, you will receive course completion certificate from Uplatz confirming that you have completed this course with Uplatz. Once you complete your learning please submit this for to request for your certificate https://training.uplatz.com/certificate-request.php
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A3. All our video courses comes with lifetime access. Once you purchase a video course with Uplatz you have lifetime access to the course i.e. forever. You can access your course any time via our website and/or mobile app and learn at your own convenience.
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A16. Software access can be purchased seperately at an additional cost. The cost varies from course to course but is generally in between GBP 20 to GBP 40 per month.
Q17. Does your course guarantee a job?
A17. Our course is designed to provide you with a solid foundation in the subject and equip you with valuable skills. While the course is a significant step toward your career goals, its important to note that the job market can vary, and some positions might require additional certifications or experience.
Remember that the job landscape is constantly evolving. We encourage you to continue learning and stay updated on industry trends even after completing the course. Many successful professionals combine formal education with ongoing self-improvement to excel in their careers. We are here to support you in your journey!
Q18. Do you provide placement services?
A18. While our course is designed to provide you with a comprehensive understanding of the subject, we currently do not offer placement services as part of the course package. Our main focus is on delivering high-quality education and equipping you with essential skills in this field.
However, we understand that finding job opportunities is a crucial aspect of your career journey. We recommend exploring various avenues to enhance your job search:
a) Career Counseling: Seek guidance from career counselors who can provide personalized advice and help you tailor your job search strategy.
b) Networking: Attend industry events, workshops, and conferences to build connections with professionals in your field. Networking can often lead to job referrals and valuable insights.
c) Online Professional Network: Leverage platforms like LinkedIn, a reputable online professional network, to explore job opportunities that resonate with your skills and interests.
d) Online Job Platforms: Investigate prominent online job platforms in your region and submit applications for suitable positions considering both your prior experience and the newly acquired knowledge. e.g in UK the major job platforms are Reed, Indeed, CV library, Total Jobs, Linkedin.
While we may not offer placement services, we are here to support you in other ways. If you have any questions about the industry, job search strategies, or interview preparation, please dont hesitate to reach out. Remember that taking an active role in your job search process can lead to valuable experiences and opportunities.
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A21. Tutor support is not available for our video course. If you believe you require assistance from a tutor, we recommend considering our live class option. Please contact our team for the most up-to-date availability. The pricing for live classes typically begins at USD 999 and may vary.