Top 5 Generative AI Myths

Generative AI (GenAI) has rapidly transformed from an emerging technology to a must-have business tool. As organizations race to implement AI solutions, misconceptions about how generative AI works, what it can do, and how to implement it effectively have proliferated. These myths can lead to costly mistakes, failed implementations, and missed opportunities.

In this article, we'll explain the top five myths about generative AI that we commonly encounter, particularly in enterprise settings. Understanding these realities will help your organization develop a more effective approach to implementing generative AI solutions.

Myth #1: It’s All About the Model

The Myth: Many organizations believe that implementing generative AI requires a model-centric approach: first selecting a model, then training it, and finally integrating it into software. This approach prioritizes building infrastructure over focusing on business outcomes.

The Reality: The model-centric approach is often inefficient and risky. Creating bespoke infrastructure for AI models:

• Requires specialized technical skills that are in short supply
• Comes with high upfront costs
• Is difficult to maintain over time
• Doesn't scale well across departments and use cases
• Creates significant risk due to the time and expense invested before proving business value

A Better Approach: Organizations that successfully scale generative AI projects focus on building common software infrastructure that allows them to:

• Experiment with multiple models quickly
• Evaluate results using real business data
• Prove value rapidly or fail fast with minimal investment
• Seamlessly transition successful proof-of-concepts into production-ready solutions

The goal should be to create a flexible foundation that enables innovation across the organization, rather than a rigid infrastructure that serves only a single use case.

Myth #2: Generative AI Is Chatbots

The Myth: Thanks to the popularity of ChatGPT, many people equate generative AI with chatbots. This narrow view limits the potential applications of the technology.

The Reality: While chat interfaces have made generative AI accessible to the masses, they represent just one medium for interaction. In professional business settings, a company-wide chatbot is not the best medium for:

• Sensitive business information
• Standard prompts serving different roles and tasks
• Complex processes requiring structured outputs
• Scenarios where deterministic, consistent outcomes are essential

A Better Approach: Consider generative AI as a versatile technology that can be integrated into:

• Existing business applications
• Automated workflows
• Document review and processing
• Decision support tools
• New product features

The interface should match the use case and organizational needs rather than defaulting to a chat-based approach for all things generative AI.

Myth #3: Models Learn from Your Data

The Myth: A common misconception is that generative AI models continue to learn from the data that users send them during interactions, potentially creating privacy and security concerns.

The Reality: Generative AI models are pre-trained foundation models and do not typically learn from the data you send them during inference. This misconception often stems from confusion between:

• GenAI models (the underlying technology)
• GenAI applications (like ChatGPT, which is built on top of GPT models)

Generative AI models have knowledge cut off dates that predate any interaction you’ll ever have with a model. The concern about data capture stems primarily from how certain GenAI applications are designed to store and learn from interaction data, and whether that data may be used to later train a newer model in the future. 

A Better Approach: Organizations should:

• Distinguish between GenAI apps that can capture data and models that cannot
• Know that generative AI models can’t learn from your data (without you fine-tuning them)
• Treat model inference like as any other cloud service where you send business data
• Understand the data handling policies of any third-party AI services

By understanding how models actually process data, organizations can make more informed decisions about implementing generative AI while protecting sensitive information.

Myth #4: Tokens Are Expensive

The Myth: Many decision-makers believe that using generative AI models is prohibitively expensive due to the cost of tokens (the units of text processed by language models).

The Reality: Like many cloud services, LLMs are rapidly becoming a commodity. While some providers like OpenAI tend to be more expensive, other options from companies like Google and Deepseek have become dramatically cheaper. In general and overall, token costs continue to decline year-over-year.

A Better Approach: Organizations should:

• Compare pricing across multiple model providers
• Consider the total cost of ownership, not just token costs
• Evaluate models based on performance-to-cost ratio
• Build a flexible architecture that allows switching between models as pricing and capabilities change

By taking a strategic approach to model selection and usage, organizations can significantly reduce costs while maintaining high-quality outputs.

Myth #5: RAG Is About Vector Search OR Graph Search

The Myth: When implementing Retrieval-Augmented Generation (RAG), many believe they must choose between vector search or graph search techniques, with debates about which approach is superior.

The Reality: While people can compare and debate search techniques, there is no single search technique that works for all search use cases. Focusing exclusively on one search technique is a recipe for poorly implemented RAG that enables hallucinations. Effective RAG implementations require retrieving information using the right search technique based on the goal of the search.

A Better Approach: Organizations should adopt a comprehensive approach that supports:

• Structured search to retrieve exact matches
• Full-text search to retrieve based on keywords and relevance
• Vector search to retrieve based on semantic similarity
• Graph search to retrieve based on relationships and patterns

The best results come from using these techniques in combination, selecting the most appropriate method based on the nature of the query and the characteristics of the data. This hybrid search strategy ensures more accurate and comprehensive information retrieval, resulting in better generative AI outputs.

Conclusion

As generative AI continues to evolve rapidly, staying informed about the realities behind these common myths will help your organization implement more effective, scalable, and cost-efficient solutions. Rather than hyperfocusing on models, fixating on chat interfaces, worrying unnecessarily about data learning, overpaying for tokens, or limiting your search techniques, focus on:

1. Building flexible, outcome-focused AI infrastructure
2. Enabling generative AI in applications and processes
3. Running GenAI model inference with the same confidence as other cloud services
4. Creating a flexible architecture that enables seamless model switching
5. Employing comprehensive retrieval techniques for RAG

By addressing these myths head-on, your organization can avoid common pitfalls and develop a more mature, effective approach to harnessing the true power of generative AI.