| S.No |
Topic |
Sub-Topics |
| 1 | Introduction to LLM Frameworks | Definition, Importance, Applications, Types of LLMs, Industry trends |
| 2 | Overview of Large Language Models | GPT, BERT, LLaMA, PaLM, Key concepts |
| 3 | Transformers Architecture | Attention mechanism, Encoder-decoder, Self-attention, Multi-head attention, Positional encoding |
| 4 | Tokenization Techniques | WordPiece, Byte-Pair Encoding, SentencePiece, Tokenization libraries, Preprocessing |
| 5 | Embedding Representations | Word embeddings, Contextual embeddings, Positional embeddings, Dimensionality, Fine-tuning |
| 6 | Pretrained Models & Frameworks | Hugging Face, OpenAI GPT, Cohere, Meta LLaMA, Integration |
| 7 | Fine-tuning LLMs | Supervised fine-tuning, Parameter-efficient tuning, LoRA, PEFT, Evaluation |
| 8 | Prompt Engineering | Prompt design, Zero-shot, Few-shot, Chain-of-thought, Best practices |
| 9 | LLM Training Pipelines | Data preprocessing, Dataset curation, Training loop, Checkpointing, Monitoring |
| 10 | Inference Optimization | Quantization, Pruning, Mixed precision, Batch inference, Latency optimization |
| 11 | Evaluation Metrics | Perplexity, BLEU, ROUGE, Accuracy, Human evaluation |
| 12 | LLM Frameworks Comparison | Hugging Face, OpenAI, Cohere, Meta LLaMA, LangChain integration |
| 13 | Integration with APIs | REST API, SDKs, Streaming, Rate limiting, Authentication |
| 14 | Vector Databases & LLMs | Pinecone, Weaviate, Milvus, FAISS, Embedding storage |
| 15 | LangChain Framework | Chains, Agents, Memory, Tools, Integrations |
| 16 | RAG (Retrieval-Augmented Generation) | Definition, Pipelines, Vector search, Integration with LLMs, Applications |
| 17 | LLM for NLP Tasks | Text classification, Summarization, NER, QA systems, Sentiment analysis |
| 18 | LLM for Code Generation | Code understanding, Generation, Auto-completion, Evaluation, Tools |
| 19 | Multi-modal LLMs | Text-to-image, Text-to-speech, Vision-language models, Applications, Frameworks |
| 20 | LLM Deployment Strategies | Cloud deployment, On-premise deployment, Edge deployment, Monitoring, Scaling |
| 21 | LLM Security & Privacy | Data privacy, Model watermarking, Access control, Compliance, Threats |
| 22 | Prompt Tuning & Instruction Tuning | Soft prompts, Instruction datasets, Fine-tuning strategies, Evaluation, Best practices |
| 23 | RLHF (Reinforcement Learning with Human Feedback) | Concept, Training pipeline, Reward model, Applications, Challenges |
| 24 | Open-source LLM Frameworks | Hugging Face, LLaMA, Falcon, MPT, Integration tools |
| 25 | LLM in Chatbots & Virtual Assistants | Conversation design, Context handling, Multi-turn dialogue, Personalization, Evaluation |
| 26 | Monitoring LLMs in Production | Logging, Metrics, Drift detection, Alerting, Performance tracking |
| 27 | Cost Optimization in LLM Usage | Compute optimization, Model selection, Batch inference, Quantization, Cloud cost management |
| 28 | Ethics & Bias in LLMs | Bias detection, Fairness, Mitigation strategies, Responsible AI, Regulatory compliance |
| 29 | Future Trends in LLM Frameworks | Multilingual models, Model scaling, Efficiency improvements, AGI research, Emerging frameworks |
| 30 | Career Path & LLM Opportunities | LLM engineer, Researcher, AI consultant, Skill development, Industry roles |
