Home/Amazon aip c01 rag architecture on amazon bedrockRAG architecture on Amazon Bedrock connects a user question to authoritative external evidence before a foundation model generates the answer. Amazon Bedrock Knowledge Bases can manage ingestion, embeddings, vector storage integration, retrieval, metadata filters, reranking, citations, structured-data queries, and Retrieve-and-Generate workflows, while applications can also use lower-level retrieval APIs when they need more control.On this pageStart with the authoritative corpusChoose parsing and chunking togetherChoose the vector store by workloadUse metadata and hybrid searchRerank valid but poorly ordered resultsChoose Retrieve or managed generationSecure the…

Home/Amazon aip c01 protecting rag from data poisoningRAG data poisoning occurs when malicious, misleading, unauthorized, or low-quality content enters the retrieval corpus and influences model responses later. The attack can be obvious, such as a fake policy document, or subtle, such as hidden text that contains an indirect prompt injection designed to make the model ignore developer instructions or misuse a tool.On this pageStart with trusted-source policyScan content before embeddingPreserve provenance and ownershipUse metadata as an eligibility boundaryDetect indirect prompt injectionKeep tools away from retrieval authorityEvaluate poisoned and conflicting casesDesign…

Home/Amazon aip c01 prompt management on amazon bedrockAmazon Bedrock Prompt management turns a prompt from an application string into a versioned AWS resource with variables, model or inference configuration, prompt variants, testing, comparison, and deployable versions. This matters because prompts can change production behavior as materially as code, yet informal teams often edit them directly in notebooks or environment variables without review or rollback.On this pageKeep the working draft mutableUse variables instead of copied promptsCompare variants during developmentCreate versions for productionVersion model configuration with the promptUse prompt caching where supportedKeep…

Home/Amazon aip c01 multi agent workflows on awsMulti-agent workflows divide a complex task among specialized agents instead of asking one large agent to understand every domain, tool, and permission boundary. On AWS, the current architectural center for new agent development is Amazon Bedrock AgentCore. AWS moved Amazon Bedrock Agents into maintenance mode as “Bedrock Agents Classic” on July 30, 2026, and recommends AgentCore for new agent workloads and future migration.On this pageSplit agents by real responsibilityUse AgentCore for new designsChoose supervisor or router behaviorUse agents as toolsKeep identities separateShare context…

Home/Amazon aip c01 latency tuning for bedrock appsLatency in a Bedrock application is the sum of several components: authentication and API edge time, model queueing, prompt processing, generation, retrieval, reranking, tool calls, agent orchestration, network hops, and client rendering. Optimizing only the model invocation can produce little user-visible improvement when the real delay is a large retrieval query or three sequential agent actions.On this pageMeasure time to first token separatelyMeasure full task latencyReduce unnecessary prompt tokensUse prompt caching for repeated contextUse cross-Region inference for capacity pressureUse latency-optimized inference cautiouslyParallelize independent…

Home/Amazon aip c01 iam for genai applicationsIAM for generative AI applications is the boundary between “the model can reason about this operation” and “the AWS account actually permits this operation.” Amazon Bedrock, Knowledge Bases, AgentCore, Lambda tools, vector stores, S3 sources, KMS keys, Secrets Manager, and application services can each require different identities and permissions. Collapsing them into one broad execution role makes agent behavior hard to contain and harder to audit.On this pageSeparate human and workload identitiesScope Bedrock model invocationProtect inference profiles and routing choicesGive retrieval only the data…

Home/Amazon aip c01 from genai prototype to production on awsA generative AI prototype proves that a model can produce a useful response. A production system has to prove far more: the right user can access it, the right model and prompt are deployed, data is governed, retrieval is current, tools are authorized, latency is acceptable, cost is bounded, failures are observable, releases are reproducible, and the application can recover when a dependency fails.On this pageFreeze the use case before scalingMove identity and data out of the notebookPut a stable API…

Home/Amazon aip c01 cost control for bedrock workloadsAmazon Bedrock cost is the result of workload shape, not one advertised token price. Model choice, input and output length, retrieval, reranking, agents, tool loops, cross-Region routing, prompt caching, provisioned capacity, evaluation jobs, embedding generation, and supporting AWS services can all contribute to the cost of one successful business task.On this pageMeasure tokens by scenarioRight-size the modelReduce repeated input with prompt cachingUse cross-Region inference for capacity, not blindlyConsider Provisioned Throughput for predictable demandControl retrieval and agent fan-outUse quotas as financial guardrailsAttribute spend to…

Home/Amazon aip c01 chunking strategies for bedrockChunking is one of the most consequential design choices in a Bedrock Knowledge Base because the retriever does not search entire documents as one unit. During ingestion, Bedrock parses content, splits it into chunks, converts those chunks into embeddings, and writes the vectors to the configured store while preserving a mapping back to the source. The chunk becomes the basic retrieval unit the application later asks the model to reason over.On this pageStart with the questions users askUse default chunking as a baselineUse fixed-size…

Home/Amazon aip c01 caching patterns for genai on awsCaching in generative AI is not one technique. An AWS application can cache repeated prompt prefixes at the Amazon Bedrock model layer, cache retrieval or tool results in an application store, cache rendered API responses where semantics allow it, and reuse static reference context so the model does not repeatedly process the same tokens. Each cache has a different freshness, privacy, and invalidation model.On this pageCache stable prompt prefixesPlace cache points deliberatelyChoose TTL by business freshnessCache retrieval results only when eligibility is…

Home/Amazon aip c01 ci cd for genai on awsCI/CD for generative AI on AWS has to version more than application code. Production behavior can change through prompts, foundation-model identifiers, inference profiles, Guardrails, Knowledge Bases, agent instructions, action groups, embedding models, retrieval settings, evaluation datasets, Lambda tools, and infrastructure. A release process that tracks only the web application can leave the most important AI behavior unreviewed.On this pageVersion prompts as production configurationVersion model and routing decisionsUse infrastructure as codeSeparate dev, test, and production dataRun model and RAG evaluation in the pipelineRun…

Home/Amazon aip c01 bedrock model evaluationAmazon Bedrock evaluations provide several ways to compare model and RAG behavior with repeatable evidence. Current Bedrock supports programmatic model evaluations, human-based evaluation jobs, model evaluation with an LLM as judge, and LLM-based evaluation of knowledge bases or external RAG sources. The platform can score built-in or custom metrics and store evaluation datasets and results in Amazon S3.On this pageBuild a representative datasetUse programmatic evaluation for repeatabilityUse LLM-as-a-judge for qualitative metricsUse human evaluation where judgment mattersEvaluate RAG retrieval and generationCompare cost and latency alongside qualityUse…

Home/Amazon aip c01 bedrock knowledge bases in practiceAmazon Bedrock Knowledge Bases provides a managed retrieval layer for RAG applications. It can ingest supported data sources, create or use embeddings, store and retrieve chunks through supported vector stores, apply metadata filtering and reranking, and combine retrieval with generation through Bedrock runtime APIs. Newer capabilities also include structured data stores that translate natural-language questions into SQL and agentic retrieval that can decompose complex questions into subqueries.On this pageStart with authoritative sourcesChoose parsing and chunking deliberatelyUse metadata for business filtersUse reranking when first-stage…

Home/Amazon aip c01 bedrock agents and tool useAmazon Bedrock Agents Classic can turn a foundation model into an orchestrator that can interpret a user request, decide which action or knowledge source is relevant, gather missing information, call tools, and return a final response. The architecture is powerful because the agent can bridge natural language and business APIs. The risk is that model reasoning now influences real systems, which makes tool design, identity, validation, user confirmation, and observability first-class engineering concerns.On this pageDesign action groups around business capabilitiesUse Lambda for controlled…

Home/Amazon aip c01 amazon bedrock model selectionAmazon Bedrock model selection is no longer a simple choice between a few text models. The Bedrock catalog includes foundation models from multiple providers, model families with different modalities and context windows, multiple API compatibility options, in-Region and cross-Region inference, inference profiles, on-demand and provisioned capacity patterns, and model lifecycle differences.On this pageStart with the taskCheck API compatibilityCheck Regional availabilityUse inference profiles intentionallyEvaluate quality with real promptsMeasure latency and throughputInclude cost in the evaluationUse Guardrails separately from model choicePlan for model lifecycle AWS’s current…