Amazon Bedrock Customization, Optimization & Automation

Explore how to fine-tune models in Amazon Bedrock using labeled datasets and S3, balancing prompt engineering and RAG for task-specific, consistent behavior.

Perform continuous pre-training on a base model with large, domain-specific unlabeled data to deepen domain understanding. Identify data sources, apply filters, preprocess, and run an Amazon Bedrock customization job.

Leverage model distillation to transfer knowledge from a larger teacher model to a smaller student model, using data synthesis and at least 100 prompts to improve latency, accuracy, and cost efficiency in Bedrock.

Evaluate Bedrock models and knowledge bases using four evaluation job types: automatic, judge model, human worker, and RAG evaluation, measuring metrics like accuracy, robustness, toxicity, and relevance with prompt datasets.

Leverage prompt caching in Amazon Bedrock to speed responses and reduce costs by caching the static prompt prefix, including system prompts and few-shot examples.

Leverage Amazon Bedrock intelligent prompt routing to route requests to the best model within a family, optimizing response quality and cost, with cross region inference for latency and availability.

Explore how Amazon Bedrock Data Automation extracts insights from unstructured content—documents, images, video, and audio—using standard output, blueprints, and customizable projects for RAG-based knowledge bases.

Analyze Lambda logs with Amazon Q Developer on the command line to download, summarize, and prioritize errors from a Lambda log group, and generate a priority plan and local reports.

Automate 500 json file updates with amazon q cli using a prompt-driven plan to add enabled, convert timestamps to unix, remove legacy settings, and verify results.

Learn to ground LLMs in truth with knowledge bases and retrieval-augmented generation in Amazon Bedrock, using OpenSearch Serverless and guardrails to mitigate hallucinations and prompt injections.

Learn how to design scalable generative AI apps on Amazon Bedrock by managing quotas and token usage, and optimize throughput with provisioning, exponential backoff, throttling, caching, queuing, and batch processing.