I just lately created a Meal Planner Agent that generates meal plans primarily based on out there groceries, dietary restrictions, and most popular cuisines whereas additionally optimizing for well being situations — a private ardour of mine. I developed the AI Meal Planner in only a few hours utilizing OpenAI’s API and Replit.
This mission bolstered an necessary lesson: turning an concept right into a proof of idea or perhaps a totally purposeful software is now simpler than ever. Nevertheless, this doesn’t diminish the necessity for builders. As a developer, your function is essential in:
- Connecting the dots between necessities and use circumstances.
- Optimizing prices by successfully managing constraints.
- Understanding massive language fashions (LLMs), immediate engineering, tooling, and integration methods.
As soon as a prototype is constructed, the first engineering challenges shift to:
- Price optimization — Managing API utilization effectively.
- Observability — Monitoring bills and output high quality.
- High quality — Guaranteeing dependable and helpful AI-generated outcomes.
- Integration and scaling — Enhancing deployment and efficiency methods.
Replit is a superb platform for creating end-to-end purposes. It provides seamless integration with OpenAI and different massive language fashions (LLMs), together with straightforward deployment and useful resource administration. Whereas my present setup shouldn’t be designed for large-scale deployment, it considerably reduces improvement time, permitting engineers to give attention to essential features like integration and tooling for AI brokers.
To reinforce the cost-efficiency of the AI Meal Planner, I applied a number of methods:
- Caching — Serving repeated requests from an area cache as a substitute of querying the language mannequin each time.
- Saving meal plans — Storing generated meal plans in a database for future use relatively than regenerating them.
- Optimizing API calls — Lowering the variety of tokens utilized in every request.
I additionally leveraged OpenAI’s instruments to observe prices at API and mannequin ranges. This helped me forecast bills and establish optimization alternatives, similar to utilizing the o3-mini mannequin, which supplies outcomes corresponding to GPT-4.0 however at a decrease price.
Every meal plan request consumed roughly 400 tokens. Based mostly on OpenAI’s pricing, I estimated the worst-case price for a high-volume software:
- ~200 requests per second (RPS)
- ~$0.75 per 1M tokens utilizing GPT-3.5-turbo (o3-mini)
- Estimated price: ~$388 monthly
For scaling this software, OpenAI’s API could be essentially the most vital price issue.
An AI agent consists of:
- LLM because the reminiscence (mind) — Processing and reasoning.
- Instruments — Executing duties primarily based on reasoning.
- LLMs reasoning and taking actions through software integrations.
- Outline a Excessive-Degree Purpose
- Create a meal-planning software that personalizes suggestions primarily based on dietary wants and promotes wholesome consuming.
2. Break It into MVP Options
- Generate meal plans primarily based on out there groceries.
- Incrementally add options similar to dietary preferences, delicacies choice, and well being situation optimization.
- Combine authentication through Google Auth/Firebase to retailer consumer preferences.
3. Iterate & Check
- Check consumer inputs and refine the mannequin’s responses.
- Repeatedly evolve options primarily based on suggestions.
4. Forecast & Plan for Scale
- Estimate API prices and infrastructure scaling wants.
- Optimize API utilization to scale back pointless LLM calls.
5. Overview & Optimize
- Enhance response high quality and observability.
- Improve reliability for scalable deployment.
I constructed this complete software in only a few hours, with an operational price of roughly $12 per day. Attempt it out right here: AI Meal Planner.
The panorama of software program improvement is shifting. AI instruments allow fast prototyping, turning concepts into working merchandise quicker than ever. Nevertheless, the actual engineering work lies in optimization/scaling, integration, observability, and high quality assurance. This mission reaffirmed that an engineer’s true worth is in what occurs after the preliminary construct — guaranteeing effectivity, scalability, and cost-effectiveness.
Exploring AI brokers within the context of my ardour for vitamin and wellness has been an thrilling journey. I additionally look ahead to exploring agent SDKs and integrating with different LLMs to check price and scalability. What is going to you construct? 🚀
