AWS Generative AI Services

Core generative AI concepts

Generative AI produces new content such as text, images, audio or code. Most text systems are foundation models: very large neural networks pre-trained on broad data and adapted to many tasks. Large language models work on tokens, which are fragments of words, and can only consider a limited number of them at once, called the context window. Embeddings turn text or images into numeric vectors so that items with similar meaning sit close together, which powers semantic search. Image generators are often diffusion models that learn to remove noise step by step, and multimodal models accept or produce more than one kind of data.

Think of the AWS offering as three layers: applications you simply use, a managed model layer you call through an API, and an infrastructure layer where you train and host your own models.

Amazon Bedrock

Bedrock is a serverless service exposing foundation models from several providers, plus AWS's own family, through one consistent API. You do not manage servers, and your prompts and outputs are not used to train the base models. Typical steps are to confirm the model is available in your Region (some providers require accepting terms first), test it in the playground, then call it from code:

aws bedrock-runtime converse \
  --model-id <model-id> \
  --messages '[{"role":"user","content":[{"text":"Summarise this ticket in two sentences."}]}]'

Choosing a model is a trade-off between quality, latency, context length, modalities and cost, so test several against your own data. Bedrock can evaluate models automatically with benchmark datasets and metrics, or with human reviewers, and you should add business metrics such as resolution rate or user satisfaction. Guardrails filter harmful content, deny specific topics, redact personal data and check responses for grounding. Invocation metrics and logs flow to CloudWatch, and access is controlled with IAM like any other service.

Prompt engineering

A good prompt usually contains an instruction, relevant context, the input data and the desired output format. Technique matters as much as wording:

• Zero-shot: ask directly with no examples.
• Few-shot: include a handful of input and output examples to set pattern and tone.
• Chain-of-thought: ask the model to reason step by step before answering, helpful for multi-step problems.
• Prompt templates: reusable prompts with placeholders, easier to version and test.
• Negative prompting: state what the output must avoid.

Inference parameters shape randomness. Temperature scales how adventurous token choice is (low for factual tasks, higher for creative ones), top-k limits sampling to the k most likely tokens, top-p limits it to the smallest set whose probabilities add up to p, and a maximum-length setting caps output size and latency. Watch for misuse such as prompt injection, where untrusted text tries to override instructions, and keep secrets and sensitive data out of prompts.

RAG and knowledge bases

Models do not know your private or recent data, and they can state wrong things confidently. Retrieval-Augmented Generation fixes this at request time: embed the user question, search a vector store for the most relevant chunks, and include them in the prompt so the answer is grounded and can cite sources.

1. Ingest documents from a store such as object storage or a web crawl.
2. Split them into chunks and create embeddings.
3. Store vectors in a vector database (managed options include OpenSearch-based and relational or graph stores with vector support).
4. At query time retrieve the nearest chunks and generate an answer.

Amazon Bedrock Knowledge Bases automate this pipeline. Chunking strategy, metadata filtering and keeping the index in sync with sources largely decide quality. RAG is usually the cheapest way to add knowledge, and it keeps data fresh without retraining.

Customising models

Try approaches in order of effort: better prompts, then RAG, then fine-tuning, and only then training from scratch. Fine-tuning adjusts a model with labelled examples. Instruction-based tuning uses prompt and response pairs to teach a task or style; continued pre-training feeds unlabeled domain text to build vocabulary and knowledge. Tuning needs good, representative data, a held-out evaluation set and a plan to re-test as base models change. Use it for consistent style or specialised tasks, not for facts that change frequently.

Amazon Q and agents

Amazon Q covers ready-made assistants. Q Business answers questions over company content through connectors, respecting existing user permissions and administrative controls, and can sign users in through Identity Center. Q Developer helps with code, explanations and AWS troubleshooting from the console or an IDE. Bedrock Agents let a model plan multi-step tasks and call your APIs or knowledge bases through defined action groups, which suits workflows like order lookup or ticket creation.

Managed AI services and SageMaker

Service	Does
Comprehend	Sentiment, entities and key phrases in text.
Translate / Transcribe / Polly	Translation, speech to text, text to speech.
Rekognition	Image and video analysis.
Textract	Extracts text and tables from scanned documents.
Lex	Conversational chatbots.
Kendra	Intelligent enterprise search.
Personalize	Personalised recommendations.
Augmented AI	Human review of low-confidence predictions.

These pre-trained APIs need no ML expertise and are often the fastest route. When you need custom models, SageMaker AI (formerly SageMaker) offers notebooks, built-in algorithms, training and tuning jobs, hosted endpoints, a model registry and monitoring. JumpStart offers pre-built models and solutions to deploy or fine-tune, and Clarify helps detect bias and explain predictions.

Responsible AI and governance

Responsible AI covers fairness, explainability, privacy, robustness, transparency, safety and governance. Concrete practices include diverse, representative training data, bias testing before and after launch, human review for high-impact decisions, and documented model limits (AWS publishes service cards describing intended uses). Remember that more interpretable models are often easier to justify but may be less accurate, so match the choice to the risk.

For security, apply least-privilege IAM, encrypt data in transit and at rest, isolate traffic with private endpoints, log invocations for audit, and track data lineage and source citations. Shared responsibility still applies: the provider secures the platform, while you own prompts, data, access and how outputs are used.