> ## Documentation Index > Fetch the complete documentation index at: https://notes.kodekloud.com/llms.txt > Use this file to discover all available pages before exploring further. # Gen AI and LLMs Introduction Part 1 > Overview of generative AI and large language models, their differences from traditional ML, enterprise use cases, and adaptation methods like prompt engineering, RAG, and fine tuning Welcome to Lesson 1 of the Bedrock Core introduction series. This article explains the fundamentals of generative AI and large language models (LLMs): what they are, how they differ from traditional supervised machine learning, typical business use cases, and the high-level choices you make when consuming pre-trained models. By the end you'll understand how GenAI systems turn prompts and enterprise data into useful outputs, and when to adapt a vendor model vs. build one. Let’s begin. ## Why generate new content with ML? Generative AI systems produce new content—text, code, images, or video—that resembles human-created outputs. To highlight how GenAI differs from classical ML, first consider common supervised tasks such as linear regression, classification, and forecasting. A presentation slide titled "Problem: How Can Machines Generate New Content?" with the subtitle "AI and ML are widely applied to problems like:". Below are three blue circular icons labeled "Linear regression," "Classification," and "Forecasting." Example: predicting house prices in London using traditional supervised learning. * Collect a labeled dataset (features: square footage, postcode, number of bedrooms; label: sale price). * Train a model to map features to the target price. * Host the trained model (for example on `Amazon SageMaker` or a container/VM) and call it at inference with new feature values to receive predicted prices. This standard supervised workflow—dataset preparation, model selection, training, validation, and deployment—requires labeled data and focused engineering effort. A presentation slide titled "Problem: How Can Machines Generate New Content?" illustrating supervised learning. It shows a flowchart: existing company data → model training → model, and new unseen data → model → prediction output. ## What is generative AI, and where is it useful? Generative AI focuses on creating new content rather than only predicting a numeric label. In enterprise settings the emphasis is pragmatic: summarization, code generation/review, question answering, or extracting knowledge from documents and communications. Organizations accumulate massive amounts of textual information—reports, emails, knowledge bases, and internal docs—that become high-value inputs for GenAI systems. Common business use cases include: * Summarize large documents for faster decision-making. * Assist developers by writing, refactoring, or reviewing code. * Power chatbots that handle repetitive customer-support queries. * Extract structured information from unstructured text. A dark blue slide titled "Problem: Real World" with the line "Organizations handle large volumes of information every day." Below are four turquoise icons labeled Document, Reports, Emails, and Knowledge Bases representing common information types. These problems often cross team boundaries—developers, support staff, and knowledge managers—but they can be addressed with GenAI models that generate context-aware outputs. A slide titled "Problem: Real World" showing two roles: Developers (with a code icon) who spend time writing and reviewing code, and Support Teams (with a hands icon) who handle repetitive queries. A small caption notes that creating content or documentation can be slow and resource-intensive. Generative AI can automate or augment content tasks, but for critical decisions, regulated workflows, or sensitive data you must include human review, validation, and governance controls. ## Why not just use traditional software or search? Traditional systems excel at storing and retrieving data and applying deterministic rules. But they do not inherently capture semantic meaning between text passages, and rule-based approaches can be brittle for language tasks that require understanding, synthesis, or creative generation. A slide titled "Problem: Real World" showing a block labeled "Traditional Software Systems" with dashed lines to three items: green checkmarks for "Store data" and "Retrieve data" and a red X for "Lack language understanding." It illustrates that traditional systems handle data storage/retrieval but fail at language understanding. ## Large Language Models (LLMs): the engine behind GenAI LLMs learn statistical and semantic patterns from massive text corpora, enabling them to generate coherent text and perform NLP tasks such as: * Summarization * Translation * Question answering * Code generation and refactoring GenAI systems are driven primarily by prompts—carefully structured inputs that guide the model to produce the desired output. Example prompts: * “Summarize this 200-page report in one paragraph.” * “Generate an image of a red sports car driving down a rainy street.” * “Refactor this Python function to improve performance.” The model will produce text, images, or other media depending on its capabilities and the prompt. A diagram titled "Solution: GenAI" showing a user prompt feeding into a generative-AI brain icon that outputs content. The outputs illustrated are text, image, and video, explaining how AI creates new content from user input. ## Typical GenAI solution workflow 1. Identify the task (summarization, QA, code generation). 2. Gather and prepare relevant data (documents, codebase, knowledge base). 3. Choose a pre-trained model or provider. 4. Adapt the model to your needs via: * Prompt engineering * Retrieval-augmented generation (RAG) to ground outputs in company documents * Fine-tuning for domain-specific behavior 5. Deploy with monitoring, validation, and human-in-the-loop checks for safety and compliance. ## How organizations consume pre-trained LLMs Most teams consume vendor‑pretrained models rather than training large models from scratch. You can adapt vendor models to your data and use case via: * Prompt engineering: craft prompts and few-shot examples to steer model behavior. * RAG: index your documents (embeddings + vector store) and include retrieved context at inference to ground answers in enterprise data. See fundamentals of RAG for implementation patterns. * Fine-tuning or parameter-efficient tuning: update model weights on domain-specific data to specialize behavior. Combining a strong pre-trained model with RAG or domain-specific fine-tuning often yields better and faster results than attempting to train an LLM from scratch. ## Common pre-trained model vendors A range of providers offer pre-trained LLMs and multimodal models: A presentation slide titled "Solution: GenAI" showing a rounded white panel with logos of companies offering pre-trained models, including Meta, Claude, Amazon, Google, OpenAI, and Deepseek. When choosing a vendor/model consider: * Capability (task suitability, multimodality) * Cost (per-inference and storage) * Latency and throughput * Data privacy, compliance, and governance * Customization options (RAG, fine-tuning, prompt controls) * Ecosystem and tooling (SDKs, integrations, managed services like Bedrock) ## Quick comparison: Traditional ML vs Generative AI | Dimension | Traditional ML | Generative AI / LLMs | | - | - | - | | Typical goal | Predict labels or values (classification/regression) | Generate or transform content (text, code, images) | | Data needs | Labeled datasets | Large unlabeled corpora + domain data for RAG/fine-tuning | | Training effort | Train models per task | Consume pre-trained models; adapt via prompts, RAG, or fine-tuning | | Common outputs | Numeric predictions, classes | Summaries, answers, generated code/images | | Best for | Forecasting, structured predictions | Language understanding, creative generation, unstructured data tasks | ## Next steps and deeper topics Future lessons will cover integration patterns for pre-trained models, how Bedrock Core helps manage access to multiple providers, implementation of RAG with vector stores, prompt engineering best practices, and deployment/monitoring strategies. ## Key takeaways * Generative AI produces new content (text, code, images) and is distinct from traditional supervised ML tasks. * LLMs are typically consumed as pre-trained models; adaptation is achieved with prompt engineering, RAG, or fine-tuning. * Real-world GenAI solutions require governance, human oversight, and careful selection of vendor/model based on capability, cost, and compliance. * Combining pre-trained models with your enterprise data (via RAG or fine-tuning) is often the most practical path to production-ready results. ## Links and References * [Amazon SageMaker](https://learn.kodekloud.com/user/courses/aws-sagemaker) * [Prompt engineering fundamentals](https://learn.kodekloud.com/user/courses/learn-by-doing-prompt-engineering-101) * [Fundamentals of RAG](https://learn.kodekloud.com/user/courses/fundamentals-of-rag) * [Kubernetes Documentation](https://kubernetes.io/docs/) This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.