Getting Started with GenAI Development: A Developer's Guide

Gartner recently reported that a staggering 80% of enterprises will be utilizing some form of generative AI, whether through models or APIs, by 2026. This news, while exciting, can also be daunting for developers who may not have direct experience building AIpowered applications. This article aims to demystify the process, providing a roadmap for developers to easily get started with GenAI.

The Three Phases of the AI Development Journey

This guide focuses on the three key stages developers will encounter when transitioning from a proof of concept to a fully functional production application:

1. Ideation and Experimentation: Exploring use cases and evaluating different models.
2. Building: Developing the application using appropriate tools and techniques.
3. Development and Operations (MLOps): Deploying, scaling, and monitoring the AIpowered application.

Phase 1: Ideation and Experimentation Finding the Right Model

The first step is identifying a specialized model suited to your specific use case. This involves:

Researching and Evaluating Models

Start by exploring popular model repositories like Hugging Face and engaging with the opensource community. Consider factors such as model size and performance, and utilize benchmarking tools to gain deeper insights.

Understanding Key Ground Rules

• Selfhosting LLMs is generally more costeffective than relying on cloudbased services.
• Small Language Models (SLMs) offer lower latency and are optimized for specific tasks compared to Large Language Models (LLMs).

Mastering Prompting Techniques

Effective prompting is crucial for eliciting desired responses from LLMs. Experiment with different techniques:

• Zeroshot Prompting: Asking a question without providing any examples.
• Fewshot Prompting: Providing a few examples of desired responses to guide the model.
• ChainofThought Prompting: Asking the model to explain its reasoning stepbystep.

It's crucial to understand the capabilities and limitations of the models you are working with. Experiment with your data early to identify potential challenges.

Phase 2: Building Your AI Application

Once you've chosen a model, it's time to build your application. Similar to databases and other services, you can run AI models locally, making API requests to localhost and keeping your data secure and private.

Data Integration Techniques

Two common methods for integrating your data with an LLM are:

• Retrieval Augmented Generation (RAG): Supplementing a pretrained foundational model with relevant data to improve accuracy and relevance.
• Finetuning: Incorporating your data directly into the LLM to customize its behavior, style, and intuition for domainspecific tasks.

Leveraging Tools and Frameworks

Tools like LangChain can significantly simplify the development process, allowing you to focus on building features like chatbots, IT process automation, and data management. These tools abstract away complexities by orchestrating sequences of prompts and model calls to achieve complex tasks.

Remember to break down problems into smaller, manageable steps and evaluate the flows during model calls.

Phase 3: Operationalizing Your AI Application (MLOps)

Deploying your AIpowered application to production and scaling it up falls under the domain of Machine Learning Operations (MLOps). Key considerations for developers include:

Infrastructure Requirements

Your infrastructure must be capable of handling efficient model deployment and scaling. Utilize technologies like containers and orchestrators such as Kubernetes to autoscale and balance traffic. Consider using productionready runtimes like VLLM for model serving.

Hybrid Approach

Many organizations are adopting a hybrid approach, using a combination of onpremise and cloud infrastructure, and employing different models for different use cases to optimize resource utilization and budget.

Monitoring and Maintenance

Once deployed, your application requires continuous benchmarking, monitoring, and exception handling. MLOps practices ensure a smooth transition of models into production.

Conclusion: AI as Another Tool in Your Developer Toolkit

Recent innovations have made AI development much more accessible. While AI is new, it's essentially another tool you can add to your skillset. By leveraging available tools and following the steps outlined in this guide, you can move from ideation to deployment and create impactful AIpowered applications.