Three Weekend Projects That Showcase LLM Integration
When I first launched jakejh.com and jjhdigital.com, I wanted to explore the potential of integrating various language models into practical applications. Instead of spending months building a single polished product, I opted for a different approach: rapid prototyping with different LLMs to explore their capabilities and implementation requirements.
The result was a series of quick projects that each demonstrated different aspects of AI integration. None of these were meant to be comprehensive products, but rather examples of what can be accomplished in extremely short timeframes with modern AI tools.
Project Chat Project: The AI Salesman
Technologies: React, NextJS, TypeScript, GPT-4, LLAMA3, Tailwind CSS
This was my first foray into AI integration this weekend—a conversational agent specifically tuned to represent JJH DIGITAL. The concept was simple: create a chatbot that could engage potential clients, answer questions about services, and even entertain visitors with its capabilities.
What made this project interesting was the fine-tuning process. Rather than building a generic chatbot, I created a detailed system prompt that:
- Provided comprehensive information about JJH Digital's services
- Established a conversational tone that matched the brand
- Included fallback mechanisms for questions outside its knowledge domain
The result was a digital "salesperson" that could handle inquiries 24/7 while showcasing the potential of conversational AI. And yes, it could even write a song about web development if you asked nicely.
Key Takeaway
Even a simple implementation of a well-prompted LLM can create valuable business tools with minimal development effort. The strength lies in the prompt engineering rather than complex code.
Project Prompt: Lightning-Fast Responses
Technologies: TypeScript, React, NextJS, Groq Inference API
For my second experiment, I wanted to explore what was possible with Groq's inference technology, which was generating buzz for its incredible speed. The result was Project Prompt—a chatbot focused on minimizing the latency between query and response.
The implementation featured:
- A customizable system prompt that users could modify
- Integration with Groq's inference API
- A clean, minimalist interface that emphasized conversation flow
What impressed me most about this implementation was how the reduced latency fundamentally changed the user experience. Conversations felt more natural and engaging when the AI responded with human-like speed, demonstrating how technical improvements in inference time directly translate to better user experiences.
Key Takeaway
Response speed significantly impacts how users perceive AI interactions. Even with identical content, faster responses create a more natural, engaging experience.
Project Planner Project: AI-Driven Itinerary Creation
Technologies: TypeScript, React, NextJS, LLAMA-70B via Groq
The most complex of my rapid prototypes, Project Planner Project was inspired by a feature I had previously built in Adventure Genie called "Genie Wishes." I challenged myself to create a similar capability—an AI travel planner that could generate personalized itineraries—in just one night.
Using LLAMA-70B through Groq's platform, I implemented:
- Multi-threaded conversation capabilities for contextual understanding
- Natural language interfaces for itinerary adjustments
- Consideration of factors like seasonality and travel times in recommendations
The application allowed users to describe their travel preferences conversationally, then receive a detailed itinerary that they could refine through further discussion with the AI.
What made this project particularly interesting was demonstrating how a feature that had originally taken weeks to develop and refine could be implemented in a basic form in just hours using modern LLM APIs.
Key Takeaway
Modern LLM APIs enable developers to compress development timelines dramatically for certain features, particularly those involving natural language understanding and content generation.
View Project Planner Project →
The Value of Rapid AI Prototyping
These three projects, while intentionally limited in scope, demonstrate an important approach to working with emerging AI technologies:
- Experimentation over perfection: By focusing on quick implementation rather than polished products, I was able to explore multiple approaches and technologies.
- Learning through doing: Each project taught me different aspects of LLM integration, from prompt engineering to managing API limitations.
- Demonstrating possibilities: These simple implementations served as powerful proof-of-concepts for clients and stakeholders to understand what's possible.
- Comparative analysis: Building similar applications with different models (GPT-4, Claude, LLAMA) provided practical insights into their relative strengths and weaknesses.
While none of these projects are production-ready applications, they represent valuable stepping stones in understanding how to effectively integrate AI capabilities into more comprehensive solutions. They're tangible examples of how modern development can leverage AI capabilities with minimal implementation overhead.
As language models continue to evolve, the ability to rapidly prototype and test implementations will become an increasingly valuable skill for developers looking to stay at the cutting edge of what's possible.