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AI Unraveled Podcast

How to develop AI-powered apps effectively.

Artificial Intelligence (AI) is rapidly transforming the tech industry, with many organizations looking to leverage AI-powered apps to gain a competitive edge. However, building an AI solution requires careful planning, the right tools, and a strategic approach to ensure that the time and resources invested are worthwhile. In this blog, we’ll explore the best practices for developing AI-powered applications effectively, focusing on maximizing productivity while avoiding common pitfalls.

Listen at https://podcasts.apple.com/ca/podcast/how-to-develop-ai-powered-apps-effectively/id1684415169?i=1000678217564

Start Small: Eating the Elephant One Bite at a Time

The process of building an AI-powered app can seem daunting. Whether you’re creating a document processor, a chatbot, or a specialized content creation tool, it’s important to break down the development process into manageable tasks. Think of it as eating an elephant—you take it one bite at a time.

One critical mistake many developers make is jumping straight into advanced AI tasks, like training or fine-tuning models. These are powerful tools, but they are time-consuming and require significant resources. Before you get there, it’s important to consider simpler alternatives that may deliver what you need.

The Power of Prompt Engineering

Prompt engineering is often underestimated. Many developers will simply enter a generic request, like “write an article about gaining muscle,” and expect magic. However, understanding that a language model doesn’t “think” or “reason” like humans is key. It predicts the next word based on its training data, meaning that the quality of the output depends largely on the input it receives.

Benefit #1: is that it can achieve similar results to fine-tuning and training your own model… But with a lot less work and resources.

Benefit #2: is that you can feed your LLM from an API with “live” data, not just pre-existent data. Maybe you’re trying to ask the LLM about road traffic to the airport, data it doesn’t have. So you give it access to an API.

If you’ve ever used Perplexity.ai or ChatGPT with web search, that’s what RAG is. RunLLM is what RAG is.

It’s pretty neat and one of the hot things in the AI world right now.

To get better results, it’s essential to carefully craft your prompts, tailoring the input to elicit the desired output. Here are some common techniques used in prompt engineering:

Assigning Roles to the LLM

A powerful strategy is to assign a specific role to the language model. For example, instead of simply asking for an article about gaining muscle, you could say, “Write an article about how to gain muscle as if you were Mike Mentzer, an expert bodybuilder.” This slight tweak can significantly improve the relevance and quality of the output by leveraging the persona of a knowledgeable source.

Alternatively, you can describe a fictional expert persona to get more tailored responses. For example, “Write as if you were an ex-powerlifter and ex-wrestler with multiple Olympic gold medals” can add depth and context to the language model’s output.

N-Shot Learning

Another technique to improve the AI’s responses is to use N-shot learning. This involves providing a few examples to demonstrate the kind of output you want. For instance, if you’re trying to write articles in a specific voice, give the model a few reference articles to learn from. This enables the AI to generalize from the examples and emulate the desired style more accurately.

If you are building an app that needs precise output (e.g., summarizing medical studies), it’s crucial to use examples that closely reflect your use case. By doing so, you help the AI learn the nuances it needs to produce high-quality, contextual responses.

Structured Inputs and Outputs

Providing structured data helps the AI interpret information better. Different formats can influence how effectively a model can parse the data. For instance, AI models often have trouble with PDF files but perform better with Markdown.

An example of effective structured input is XML. Consider this input:

<description>
The SmartHome Mini is a compact smart home assistant available in black or white for only $49.99. At just 5 inches wide, it lets you control lights, thermostats, and other connected devices via voice or app—no matter where you place it in your home.
</description>

If you ask the AI to extract the <name>, <size>, <price>, and <color> from this description, the structured context makes it easy for the AI to parse and understand what each element represents. Structured inputs are particularly helpful for AI-powered apps that rely on extracting key data from a well-defined source.

Chain-of-Thought Reasoning

Chain-of-thought is another powerful concept for improving AI performance. By explicitly instructing the model to “think step by step,” you can often get more comprehensive and accurate responses.

For example, in a chatbot aimed at providing medical advice, you might use the following system prompt:

You are an expert AI assistant specializing in testosterone, TRT, and sports medicine research. Follow these guidelines:

1. Ask clarifying questions.
2. Confirm understanding of the user's question.
3. Provide a clear, direct answer.
4. Support with specific evidence.
5. End with relevant caveats or considerations.

SYSTEM_PROMPT = """You are an expert AI assistant specializing in 
testosterone, TRT, and sports medicine research. Follow these guidelines:

1. Response Structure:
- Ask clarifying questions
- Confirm understanding of user's question
- Provide a clear, direct answer
- Follow with supporting evidence
- End with relevant caveats or considerations

2. Source Integration:
- Cite specific studies when making claims
- Indicate the strength of evidence (e.g., meta-analysis vs. single study)
- Highlight any conflicting findings

3. Communication Style:
- Use precise medical terminology but explain complex concepts
- Be direct and clear about risks and benefits
- Avoid hedging language unless uncertainty is scientifically warranted

4. Follow-up:
- Identify gaps in the user's question that might need clarification
- Suggest related topics the user might want to explore
- Point out if more recent research might be available

Remember: Users are seeking expert knowledge. Focus on accuracy and clarity 
rather than general medical disclaimers which the users are already aware of."""

Incorporating chain-of-thought prompts, particularly in complex scenarios, can result in richer, more informative output. The downside, of course, is that this may increase latency and token usage, but the improved accuracy can be well worth it.

Breaking Down Large Prompts

For complex, multi-step processes, it’s often effective to split a large prompt into multiple smaller prompts. This approach helps the model focus on each specific part of the task, leading to better overall performance. For example, tools like Perplexity.ai leverage this strategy effectively, and you can adopt the same approach in your AI projects.

Utilizing Relevant Resources: Retrieval Augmented Generation (RAG)

Another method to enhance AI-powered apps is to provide the model with external data. This is where Retrieval Augmented Generation (RAG) comes into play. With RAG, you can inject additional, up-to-date information that the model wasn’t trained on. For example, you might want the AI to help with a new SDK launched last week—if the model was trained six months ago, that information would be missing. Using RAG, you can provide the necessary documentation manually.

RAG has several core advantages:

1. Cost-Effectiveness: RAG can achieve similar results to fine-tuning without the need for intensive training or resource usage.
2. Real-Time Integration: You can feed the model live data via an API, which can be highly useful for tasks like checking current traffic or real-time stock updates.

RAG-based implementations are commonly seen in tools like Perplexity.ai and ChatGPT’s web search. These use strategies such as vector embeddings, hybrid search, and semantic chunking to enhance the performance of the language model with minimal manual input.

Conclusion

Building effective AI-powered apps doesn’t have to be overwhelming. By using foundational techniques like prompt engineering, structured inputs, chain-of-thought, and Retrieval Augmented Generation (RAG), you can significantly enhance the performance of your AI applications. It’s all about strategically employing the tools available—starting with simpler techniques and moving to more advanced methods as needed.

Whether you’re creating a simple chatbot or a complex automation tool, these best practices can help you develop AI apps that deliver value, are efficient, and make the most of the available technology.

References: 

1- Reddit

2- AI and Machine Learning For Dummies

AI Consultation:

Want to harness the power of AI for your business? Etienne Noumen, the creator of  “AI Unraveled,” is also a senior software engineer and AI consultant. He helps organizations across industries like yours (mention specific industries relevant to your podcast audience) leverage AI through custom training, integrations, mobile apps, or ongoing advisory services. Whether you’re new to AI or need a specialized solution, Etienne can bridge the gap between technology and results. Contact Etienne here to learn more and receive a personalized AI strategy for your business.

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