Amazon Bedrock & Foundation Models | Generative AI, LLMs, RAG, Agents, Fine-Tuning & Evaluation

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This is a full-length deep dive (1:16:28) into Generative AI, Amazon Bedrock, and Foundation Models — designed for AWS exam success and real-world understanding.

We start by clarifying AI vs Machine Learning vs Deep Learning vs Generative AI, then move into Foundation Models on Amazon Bedrock: what they are, why pre-training is expensive, how Bedrock model access works, and how token-based pricing is structured.

From there we cover the core concepts behind Large Language Models (LLMs): inference, context windows, tokens, embeddings, and how text generation happens one token at a time. We also explain diffusion models and the main GenAI image use cases (text-to-image, image-to-text, image-to-image).

Finally, we tie it together with the foundation model lifecycle, how to select a model, and the major customisation approaches you must know for the exam: prompt engineering, inference parameters, RAG, agents, fine-tuning, and training — plus how models are evaluated using human evaluation, ROUGE, BLEU, and BERTScore.

🔹 Key Topics Covered:

Generative AI and foundation model fundamentals

Amazon Bedrock: model access, providers, token pricing

LLM basics: inference, context windows, tokens, embeddings

Diffusion models + GenAI image workflows

Foundation model lifecycle (data → pre-train → fine-tune → iterate)

How to choose a foundation model (cost, modality, latency, compliance)

Customisation: prompts, inference params, RAG, agents, fine-tuning, training

Prompt risks (prompt injection, output manipulation)

Evaluation: human, ROUGE, BLEU, BERTScore

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⏱️ Timestamps (1:16:28 runtime)

00:00 – Intro: Where Generative AI fits (AI vs ML vs DL vs GenAI)
03:40 – What Generative AI does (creating new data) + key use cases
08:10 – Foundation Models explained (pre-training concept, why it’s costly)
13:20 – Amazon Bedrock overview: model access, providers, base models
18:10 – Bedrock pricing: tokens, cost considerations, picking cheaper models
23:10 – Foundation model lifecycle: data collection → pre-train → fine-tune → iterate
30:30 – LLM basics: self-supervised learning + predicting the next token
36:40 – Inference explained (token-by-token generation)
41:10 – Context windows: limitations, trade-offs, cost implications
45:10 – Tokens and embeddings: tokenisation + meaning as vectors
51:20 – Transformers: what they are (high-level, exam-safe explanation)
55:40 – Bedrock text generation examples: summarisation, ads, extraction, PII removal
01:01:10 – GenAI for images: text-to-image, image-to-text, image-to-image
01:05:30 – Diffusion models: noise addition/removal and exam association
01:09:00 – Selecting a foundation model: cost, modality, latency, compliance, scaling
01:12:10 – Customisation methods: prompt engineering, inference params, RAG, agents
01:14:50 – Fine-tuning vs training, transfer learning terminology (exam note)
01:15:40 – Evaluation methods: Human vs ROUGE vs BLEU vs BERTScore + summary

🧠 Hashtags

#AmazonBedrock #FoundationModels #GenerativeAI #AWS #LLM
#RAG #AIAgents #FineTuning #AWSExamPrep #CloudWolf