AI Quick Reference

Looking for fast answers or a quick refresher on AI-related topics? The AI Quick Reference has everything you need—straightforward explanations, practical solutions, and insights on the latest trends like LLMs, vector databases, RAG, and more to supercharge your AI projects!

• If my application requires a feature or capability that Bedrock doesn't currently support (such as a specific model or more fine-grained control), how should I approach this limitation or find a workaround?
• What if the Bedrock model outputs content that violates my application's content guidelines or policies (how can I detect and handle such outputs)?
• Why am I not seeing my fine-tuned model appear as available for inference after the training job has finished on Bedrock?
• What strategies can be used to improve the quality of model outputs without significantly increasing latency (for example, using better prompts vs. switching to a larger model)?
• How do model updates or upgrades on Bedrock (like when a newer version of a model is released) affect performance, and what should I do to adapt to these changes?
• How do I deploy or use a custom fine-tuned model from Bedrock for inference once the fine-tuning job is complete?
• What factors influence the latency of a model's response on Amazon Bedrock, and what can I do to reduce any delays?
• What is the typical throughput (requests per second or tokens per second) one can expect from Bedrock for a given model, and can this throughput be increased through any configuration?
• How do I address memory or performance issues on my client side when handling very large responses returned by Bedrock models?
• How do I tune generation parameters such as maximum tokens, temperature, or top-p to balance output quality and generation speed on Bedrock?
• How can I call an Amazon Bedrock-provided model (for example, Jurassic-2 or Anthropic's Claude) via the AWS SDK or AWS CLI?
• How do I capture and handle errors or exceptions when making requests to the Bedrock service in my code?
• How do I debug a situation where Bedrock's responses are inconsistent (for example, sometimes they are accurate and other times nonsensical for similar inputs)?
• If Bedrock's generative model outputs contain factual errors or hallucinations, what steps can I take in my application workflow to detect and correct these?
• In the context of Bedrock, how can I evaluate whether using a large generative model via the service is the most efficient solution, or if a smaller specialized model (possibly outside Bedrock) would be more cost-effective for my specific task?
• How do I determine if an issue is on the Amazon Bedrock service side (for example, a service outage) versus an issue in my own implementation?
• How can I effectively load test a Bedrock-powered API to assess how it performs under heavy usage?
• How can I ensure consistent performance and output quality as the number of requests to Bedrock scales up (avoiding degradation under load)?
• What are best practices to ensure efficient training (fine-tuning) on Bedrock, such as using an appropriately sized dataset or choosing optimal hyperparameters to reduce training time and cost?
• What is the process to fine-tune or customize a model through Amazon Bedrock with my own dataset?
• What are some best practices for writing prompts when using Amazon Bedrock's language models to get good results?
• How do I get started with Amazon Bedrock — what are the steps to enable or access it in my AWS account?
• How should I handle exceptions thrown by the AWS SDK when calling Bedrock (such as ServiceUnavailable errors or throttling exceptions)?
• How should I handle very large output requirements or long-form content generation in Bedrock (for instance, requesting a lengthy essay) in terms of performance and reliability?
• How can I incorporate feedback or a human-in-the-loop process with Bedrock outputs (for example, reviewing generated content and refining prompts)?
• How can I incorporate Amazon Bedrock into a CI/CD pipeline for my application (for example, automating deployment of configuration changes or model updates)?
• How can I integrate Amazon Bedrock into a larger application architecture (for example, calling Bedrock from an AWS Lambda function or an API backend)?
• How do I integrate Bedrock with other AWS services (like AWS Step Functions or EventBridge) to build end-to-end AI-driven workflows?
• How can I use result filtering or output truncation to manage performance if a model's output tends to be excessively long or verbose?
• What options do I have to compress or limit the size of inputs and outputs to keep Bedrock interactions efficient (for example, truncating unnecessary context or reducing image resolution)?
• What are best practices to minimize the cost when using Amazon Bedrock, especially for applications with high request volumes?
• What does it look like to monitor a fine-tuning job on Amazon Bedrock (where can I see the job status or logs)?
• How can I monitor and measure the performance of my Amazon Bedrock requests (for instance, tracking response times, token usage, or error rates)?
• How can I optimize the performance (especially latency) of model responses when using Amazon Bedrock in my application?
• How can I optimize prompt design to get the desired result more efficiently (for example, obtaining correct outputs without needing multiple back-and-forth calls or extremely long prompts)?
• How do I prepare and format my training data for fine-tuning a foundation model on Bedrock (for example, using JSONL files with prompt-completion pairs)?
• How can I retrieve the list of available models or model versions programmatically via the Bedrock API?
• How can I secure my Bedrock usage so that only authorized applications or users can call it (for example, using IAM policies or endpoint restrictions)?
• How do I set parameters like maximum tokens, temperature, or top-p for text generation when using a model via Bedrock?
• How do I specify which foundation model to use in a request to Amazon Bedrock (for example, choosing between different model IDs)?
• What steps are needed to test and validate the outputs of a Bedrock model in a development environment before deploying to production?
• How do I troubleshoot a situation where a fine-tuning job on Bedrock fails or does not complete successfully?
• How can I troubleshoot issues with how I'm formatting prompts or instructions that might cause Bedrock to misinterpret my request?
• How can I troubleshoot network or connectivity issues that prevent my application from reaching the Amazon Bedrock endpoint?
• How can I use Amazon Bedrock from a Python application? Is there an AWS SDK (like Boto3) support or specific library for it?
• How can I use Amazon Bedrock in a workflow to process documents (for example, summarizing text from documents stored in S3 and then saving the results)?
• What AWS IAM permissions or roles are required to be able to use Amazon Bedrock in an application?
• What is the process for updating or retraining a model that I've customized on Bedrock when I have new training data (continuous improvement)?
• What should I do if Amazon Bedrock returns an error message or error code in response to a model invocation request?
• Why might one of the model providers in Bedrock (say, AI21's model or Anthropic's model) not be returning results or encountering errors while others work fine?
• How do you decide which model to use for a given task within Amazon Bedrock (for example, choosing between Claude, Jurassic, or a Titan model)?
• What metrics should I consider when evaluating the performance of generative models on Bedrock beyond just speed (for example, output quality metrics or cost per request)?
• Are there concurrency best practices for using Bedrock, such as whether to use multiple parallel requests or queue requests to achieve better throughput?
• Can Amazon Bedrock be used for code generation or assisting developers with programming tasks (for example, providing code suggestions or documentation)? If so, how might that work?
• Can Amazon Bedrock be used to implement a multi-modal application that takes both image and text input (or produces multi-modal output), and if so, how might that work?
• Does Amazon Bedrock integrate with other AWS services (like linking outputs to AWS Lambda, storing prompts/results in S3, etc.) as part of an application workflow?
• Does Amazon Bedrock support scaling up for high-throughput scenarios, and what steps should I take to ensure my application scales effectively with Bedrock?
• Is it possible to get token usage metrics or other usage details from Amazon Bedrock after making a request (to track costs or performance)?
• Can Amazon Bedrock responses be cached for repeated queries, and would caching improve efficiency for certain use cases?
• Does the AWS region in which I use Bedrock affect performance (for example, would selecting a different region reduce latency for my user base)?
• Are there differences in performance considerations between Bedrock's text generation tasks and image generation tasks, and how can each be optimized?
• How does Amazon Bedrock simplify the process of building and scaling generative AI applications for developers?
• How does Amazon Bedrock compare to other cloud offerings (such as Microsoft Azure's OpenAI Service or Google Vertex AI) in providing foundation model access?
• How does Amazon Bedrock incorporate safe AI practices, like filtering or moderating content generated by the models?
• How do the recently announced Amazon Nova models relate to Amazon Bedrock, and will they be available through the Bedrock service?
• How can I generate an image or other non-text content using Amazon Bedrock, if the service supports models like Stable Diffusion?
• How do I handle multi-turn conversations with a model via Bedrock — do I need to manually maintain and send the conversation context with each request?
• What are some use cases of Amazon Bedrock in content moderation or ensuring that generated content follows certain policies or guidelines?
• How can Amazon Bedrock help with localization or translation tasks using its generative language models?
• In what ways can Amazon Bedrock help reduce the time-to-market for AI-driven products or services by offloading infrastructure and model management?
• How does the choice of model in Bedrock (for example, using a larger model vs. a smaller one) affect the response time and throughput of requests?
• How can I handle rate limits or throughput limits in Bedrock to avoid throttling in a production system?
• How can I optimize the cost-performance ratio when using Bedrock, for example by selecting the right model provider or adjusting generation settings like temperature or max tokens?
• How can I resolve issues when I encounter a "model not found" or "unsupported model" error in Bedrock?
• What are common mistakes or misconfigurations that could cause a Bedrock integration to fail (such as wrong endpoint URLs, incorrect request payload format, or missing parameters)?
• What are predictive AI agents?
• What is a cognitive AI agent?
• What is a deliberative agent in AI?
• What is a learning agent in AI?
• What is a rational agent in AI?
• How do AI agents adapt to new environments?
• What is the difference between AI agents and bots?
• What is the difference between AI agents and expert systems?
• How are AI agents used in robotics?
• What are the main use cases of AI agents?
• What are some examples of AI agents in everyday life?
• How do AI agents work?
• How do AI agents balance computational efficiency and accuracy?
• How do AI agents balance exploration and exploitation?
• What are the different types of AI agents?
• What algorithms are commonly used in AI agents?
• How do AI agents communicate with other agents?
• How do AI agents contribute to knowledge discovery?
• How do AI agents enable conversational AI?
• How do AI agents contribute to adaptive learning systems?
• How do AI agents improve cybersecurity defenses?
• How do AI agents support disaster management solutions?
• How do AI agents handle adversarial environments?
• How do AI agents handle conflicting goals?