generate an image of the fierce battle of warriors and the dragon Assistant # Initialize the canvas canvas = Canvas() # Set a global description for the canvas canvas.set_global_description( description='A fierce battle between warriors and a dragon.', detailed_descriptions=[ 'In this intense...
Chat GPT-4’s capacity to analyze large data quantities and provide insights can be valuable. Recognizing patterns and trends in extensive datasets can lead to more accurate conclusions and better-informed decisions.
Instead, these models generate responses based on probabilities and patterns they’ve learned frommassive amounts of data. This means that clear and precise instructions are essential to steer the AI towards providing the most accurate and useful answers. It’s more like an interactive game than a...
How would you build an LLM-powered enterprise search system? Design an in-memory database. Design a web hook system. System design "knowledge" questions (for fresher/junior engineers) The second type of system design questions is “knowledge” questions to test your knowledge about key system ...
In the evolving world of Large Language Models (LLMs), crafting effective prompts has become an essential skill. That's why I've created this collection, showcasing the most impactful prompts of the year across various intriguing domains. 🌐 - bigprof-
Have you considered AI coding assistants? These powerful tools encourage you to rethink productivity by enabling faster, more accurate code writing, while also freeing up time for creative solutions to the challenges you face. For example, using Amazon CodeWhisper in Visual Studio Code, you can wr...
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The task description for AI is embedded within the prompt itself. Prompt Engineering is effectively crafting well-structured and precise prompts ensures accurate and context-aware AI responses. Reference (unaffiliated) with more detailed examples and guidelines (Browse with caution!): https://hackr.io...
This helps the AI understand the intention and constraints of each parameter, making tool calls more accurate and reliable. You can alos ommit Annotated and just pass the Field parameter. def get_weather( location: str = Field(description="The city and state, e.g. San Francisco, CA"), ...
(ObjectResponse): title: str content: str summary: str word_count: int # Initialize agent web_agent = Agent( "Web Content Analyzer", model="openai/gpt-4o", # You can use other models ) # Create a task to analyze a web page task = Task( description="Fetch and analyze the content ...