Here is what I used for the system prompt: To use the API, you have to create a prompt that leverages a "system" persona, and then take input from the user. OpenAI recently just released their ChatGPT API, which is 10x cheaper than their previous text generation API. Prompt engineering is about designing prompts that elicit the most relevant and desired response from a Large Language Model (LLM) such as GPT-3.Ĭrafting these prompts is an art that many are still figuring out, but a rule of thumb is the more detailed the prompt, the better the desired outcome. These data cleaning steps helped to refine the raw data and enhance the model's overall performance, ultimately leading to more accurate and useful insights. By removing these short, context-less snippets, I ensured that the data fed into the model was both relevant and meaningful. This enabled the model to process the information effectively without losing any essential data.įiltering Out Short Texts: I also filtered out any text with less than 13 tokens, as these often comprised service names, like "Amazon S3," without any contextual information. Token Limitation: Since the model I used has a token limit of 5,000, I divided sections exceeding this limit into smaller chunks. This step facilitated better analysis and processing by the model. Text Normalization: To ensure consistency, I performed text normalization by converting all text to lowercase and removing any extra spaces or special characters. In this project, I focused on three primary data cleaning techniques:
This seems like a natural candidate for AI, which could detect changes in patterns in order to highlight issues that demand human attention.Data cleaning is a vital step in preparing scraped data for analysis, ensuring that the resulting insights are accurate, relevant, and meaningful.
The challenge in configuring alerts is to get the right balance between too much information (risking that real problems get lost in the flood) or not enough (problems are not surfaced at all). The documentation specifically states that you cannot send SNS notifications from unsupported services, send arbitrary text, or run AWS CLI commands – this last one could give huge power to the bot, not a good idea.Īnother disappointment is that AWS does not seem to be putting effort into any AI capabilities.
Note that AWS does not appear to envisage sending commands through the bot that do anything other than retrieve diagnostics. Your teams will be able to analyze and respond to events faster by retrieving diagnostic information on command in a centralized place.Ī chatbot that is non-interactive is not much of a chatbot. All we know is that: In the near future AWS Chatbot will make it easy to retrieve diagnostic information about your AWS resources for many AWS services. We wasted a bit of time looking for detail on the ability to "execute commands to return diagnostic information" promised in the announcement, before spotting that this is not available in the beta. That said, the "ask for diagnostics" part of Chatbot, which has not yet been pushed out, is the most interesting feature. Watching a Slack channel means you can see who else is responding or seek advice from other team members. Preconfigured integration with Slack may be handy (though there's no Microsoft Teams support) since it alerts a team in one place, assisting collaboration. The existence of CloudWatch and SNS means that it is not difficult to receive alerts from your AWS services, so some might question the point of getting alerts from Chatbot. The notification has to originate from a supported service though, which could be CloudWatch, Billing and cost management, CloudFormation infrastructure management, GuardDuty threat monitoring, AWS Health performance monitoring, or Security Hub, which combines GuardDuty and other security alerts. Chatbot uses other AWS services to send alerts to Slack or ChimeĬhatbot also currently lets you receive messages from SNS in a Slack channel or in an Amazon Chime chatroom.
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