Early in 2023, OpenAI unveiled ChatGPT, a game-changing advancement in the world of conversational AI. Within a year, this innovation transformed from a mere product to a household name, becoming indispensable when interacting with Artificial Intelligence.
The Rise of Generative AI
The evolution of AI, especially with the introduction of GPT-4 and the rise of building LLM applications, has been astounding. Professionals across diverse sectors have integrated ChatGPT into their workflows, some adopting it even without official endorsements from their organizations. LLM MVPs and LLM prototypes have become essential tools in this new age.
But with great power comes great responsibility. The backdrop of ChatGPT, being run by OpenAI’s closed-source LLM, raises inevitable questions on data privacy, security, memory, and the much-debated customization capabilities. Recognizing this need, a new wave of generative AI application development firms has emerged, aiding businesses of all scales in designing custom applications on ChatGPT, thus optimizing the immense potential of AI. This includes LLM application development and custom LLM applications tailored to specific LLM use cases.
Understanding the Need: Why Businesses are Turning to Custom ChatGPT Applications
OpenAI offers two primary interfaces for ChatGPT,
Conversational Chatbot: This is an interactive platform where users can directly dialogue with GPT, enriched by various plugins like advanced data analysis and DALL·E.
OpenAI Playground: A playground for AI enthusiasts, it allows tweaks in the system and user messages, adjustment of temperature settings, and observation of how these modifications influence the AI’s outputs.
However, modern businesses crave more than just a generic AI interface. They aspire for AI agents that can automate and streamline their operations. Key use cases include:
- Crafting persuasive emails and blog posts with text drafting AI. (Eg: Jasper.ai, Copy.ai, Writesonic)
- Enhancing customer relations with AI-driven Customer Support (Eg, Chatbase, ChatDot by Lyzr.ai)
- Seamlessly navigating through vast organizational data with the Knowledge Base Access (Eg: KnowledgeQA by Lyzr.ai)
- Creating compelling content, both textual and visual, using the combined might of ChatGPT and DALL·E
- Extracting actionable insights from raw data with AI-powered Data Analysis (Eg, NeoAnalyst.ai, ThoughtSpot)
- Customizing AI solutions for varied departments and industries, from HR and e-commerce to healthcare and insurance.
The Next Frontier: Auto AI Agents and Beyond
The horizon of AI is ever-expanding. The current buzz revolves around automated AI agents, with frameworks like BabyAGI, AutoGPT, and Microsoft AutoGen leading the charge. On the application development front, platforms like Langchain, LlamaIndex, and LyzrAI are enabling rapid application development processes with their SDKs.
The 3 Popular Methods For Building Generative AI Applications
Now, let’s look at the three popular methods of LLM applications built on ChatGPT use. LLMs employ three primary methodologies:
Prompt Engineering: With GPT-4’s extensive contextual prowess, prompt engineering has taken center stage. The chain-of-density technique (based on a paper published by Salesforce), the chain-of-thought technique (published by researchers), and the SSR (Split-Summarize-Rerank) prompting model (published by the Lyzr AI team) are some of the many advanced prompting techniques available to build a straight-forward yet effecting AI application.
Retrieval Augmented Generation (RAG): The current favorite among businesses, RAG stands out for its memory-handling capabilities. Storing context in vector databases like Pinecone, ChromaDB, Weaviate, PGVector, Milvus, and RAG offers a rapid and accurate information retrieval system. RAG plays a major role in reducing the hallucination of ChatGPT-powered applications. Read more on RAG from the blog posts below.
- RAG: Revolutionizing LLM Applications for Accurate Real-time Responses
- Using RAG Workflow to Retrieve Information Effectively
- Breaking Down OpenAI and Vector Embeddings
Fine-Tuning: Fine-tuning tweaks an existing LLM, like GPT-3.5 or LlaMA2, using specific training data, ensuring better outputs and reduced costs. While building a proprietary LLM gained momentary traction with Meta’s LLaMA version, businesses soon realized the associated costs and complexities. The consensus now leans towards leveraging established platforms like OpenAI and Anthropic.
Developing a ChatGPT-Based Generative AI Application in Just 8 Weeks and for just $75,000
Success in AI-driven projects hinges on an implementable usecase with measurable outcomes. Our approach at GoML.io is a blend of understanding, refining, and iterative development. We’ve crafted over 50 Generative AI applications (just in the last four months), armed with proven templates and techniques, ensuring you have a working prototype in just eight weeks.
A typical sprint will look like the following:
- Week 1: Finalize the usecase and outcome requirements
- Week 2 thru Week 7: Iterative development with weekly feedback meetings
- Week 8: User acceptance testing and go-live
- Week 9-10: Post-production hypercare for bugs and uptime
What are the deliverables?
- Source code of the Generative AI application (both the backend and the frontend)
- A lightweight User Interface that allows the complete demo of the prototype (we can help you with advanced UI UX with the help of our design partners)
- Detailed documentation with support document
- An option to continue the engagement by moving to a retainer model where you get a team of 3 Generative AI Engineers who will continue to build the current application or build new applications
Taking the Next Step
To unlock the AI potential for your business, schedule a call with our founding team. Let’s redefine the future together!