Leveraging AI tools efficiently is no longer optional—it’s essential. But gettingpracticalanswers from LLM isn’t always about the toolor what you ask; it’s abouthow you ask—the secret lies in craftingprecise, actionable promptsthat align withyour goals.
Here’s a quick guide to help you ask the right questions and unlock maximum value,all while maintaining a practical, results-oriented approach.
1. Be Specific andProvide Context
AI thrives on specifics.Give as much relevant context as possibleif you want clear,actionable answers. Ambiguity leads to vague responses, while specificity ensuresprecision.
Example
How do I optimize file uploads?
How can I optimize the file upload flow for an Angular +Fast API+ PostgreSQLarchitecture deployed in Azure AKS?When the context is embedded in your question, the response will be tailored to fityour use case. Avoid generic prompts—they only add morebackandforth.
When the context is embedded in your question, the response will be tailored to fityour use case. Avoid generic prompts—they only add morebackandforth.
2. Define Scope and Depth Upfront
Not every query requires a deep dive. Some answers need conceptual guidance,while others require code snippets or hands-on solutions. Defining the expecteddepth ensures you get exactly what you’re looking for.
Example
ExplainFast API
Give me a high-level overview of Fast API routers, focusing on parameter validation and error handling.
3. Make Your Preferences Known
Communicate your preferences early—whether it’s a preferred design pattern,framework, or style. This avoids solutions that don’t align with your objectives.
Example
How should I handle file uploads?
I prefer modular design with minimal coupling. How can I handle file uploads in Fast API while keeping the logic away from routers?
Your personal preferences shape the solutions you receive—don’t leave them out!
4. Ask for Examples or Structured Outputs
Clarity improves when you define how the response should be structured. Asking foralist, step-by-step guidance, or a specific format helps shape the outcome.
Example
How do I use Python-type hints?
Provide a Python example using type hints for a function that takes a list ofintegers and returns their sum.
Structured outputs likecode snippets or templates are far more effective whenlearning new concepts.
5. Incorporate Constraints or Tools
Example
If the solution depends on specific tools, frameworks, or platforms, mention themupfront to get relevant answers.
How do I log API calls?
How can I log API calls in myFast APIbackend using OpenTelemetry, Loki, andGrafana?
6. Ask for Trade-offs or Alternatives
Sometimes the best solution comes from exploring options. When comparingapproaches, askingfor trade-offsto makeinformed decisionsis helpful.
Example
Should I use DAPR?
What are the pros and cons of using DAPR for cloud-agnostic communicationcompared to native Azure Service Bus integration?
7. Use Contextual Follow-ups
Complex systems often requiremulti-step discussions. Follow-up prompts that buildon previous questions maintain continuity and yield better results.
Example
● First prompt: Explain API routing inFast API.
● Follow-up: How can I divide the layers into router, service, and data modelswhile keeping the router logic clean?
This sequential approach allows deeper dives without overwhelming the initial query.
8. Share Drafts for Review and Improvement
When working on deliverables, sharing a draft can unlock insights and ideas forimprovement. AI canfunction asa sounding board for revisions.
Example
Help me write a LinkedIn post about career lessons from David Patterson.
Here’s a draft of my postabout career lessons inspired by David Patterson. Canyou refine it for clarity and impact? [Insert draft]
9. Break Down Complex Queries
When dealing with multi-part questions, breaking them down ensures each aspectgets the attention it deserves.
Example
How do I scale my Fast API app in Kubernetes and improve observability with Open Telemetry?
How can I scale aFast APIapp in Kubernetes for better load handling? (After response)
Now, how can I integrate OpenTelemetry for improvedobservability in the samesetup?
10. Ask for Critique or Feedback
Sometimes, the answer you seek is not about what to do—but how to improve.Feedback requests work best when they are focused on specific areas.
I struggle with work – related conversations.
I’m confident in casual chats but lose my edge during work discussions. Can yousuggest ways to boost my confidence in those moments?
By focusing on the challenge, you steer the AI towards practical, actionable advice.
Conclusion
Writingefficient prompts isn’tabout askingquestionsbutcrafting them withprecision, clarity, and intent.Considerdefining the requirements before building asolution—ambiguous inputs will lead to scattered outcomes. When your promptsreflect a clear understanding of what you want, the AI becomes a toolandacollaborative partner in problem-solving.
Master this approach, and you’ll find yourself cutting down the time spent clarifyingquestions, getting to the solutions faster, and unlocking the true potential of AI inyour day-to-day tasks.
Do you have any interesting prompts, tips, orideas to share? Please comment belowor write to us at hello (at) goaiiq.com to make AI work for your business.