Unlock LLMs: Naming is Key to Discoverability

Large language models (LLMs) are rapidly transforming industries, but their true potential remains locked without effective LLM discoverability. Making these powerful tools accessible and understandable is paramount. Are we truly ready to unlock the full potential of these digital brains, or are we leaving innovation on the table?

1. Standardize Naming Conventions

The first, and arguably most crucial step, is establishing consistent naming conventions. This might sound simple, but inconsistent naming is a major obstacle to LLM discoverability. I’ve seen countless organizations where different teams use completely different names for the same model, or use internal jargon that’s meaningless to others.

Pro Tip: Develop a naming convention document and enforce it across all teams. Include elements like model version, intended use case, and training data source. For example, instead of “Project Phoenix,” use “SentimentAnalysis_v2.1_CustomerReviews.”

A well-defined naming convention should include:

• Model Function: Clearly describe the primary task the LLM is designed for (e.g., “TextSummarization,” “CodeGeneration”).
• Version Number: Use a numerical system (e.g., v1.0, v1.1) to track updates and improvements.
• Training Data: Indicate the type or source of data used for training (e.g., “FinancialReports,” “MedicalLiterature”).
• Team/Department: Include a short identifier for the team responsible for the model.

2. Create a Centralized Repository

Once you have a consistent naming system, you need a central location to store and access your LLMs. Think of it as a digital library for your AI tools. This repository should contain not only the models themselves, but also detailed documentation, example prompts, and API specifications.

We use DataPlatform at my current firm, and it’s been a lifesaver. We configured it to automatically index new LLMs based on their naming conventions. This allows anyone in the company to quickly find the model they need, along with all the information required to use it effectively.

Common Mistake: Storing LLMs in disparate locations (shared drives, individual servers) without a central index. This makes it incredibly difficult to find and reuse existing models.

Your repository should include:

• Model Card: A comprehensive document outlining the model’s purpose, capabilities, limitations, training data, and performance metrics.
• API Documentation: Clear instructions on how to access and interact with the model via API.
• Example Prompts: A collection of example prompts demonstrating how to effectively use the model for different tasks.
• Code Snippets: Sample code in various programming languages (Python, Java, etc.) to help developers quickly integrate the model into their applications.

3. Implement Semantic Search

Traditional keyword-based search can be inadequate for finding LLMs. You need a search engine that understands the meaning behind the search query, not just the keywords. This is where semantic search comes in. Semantic search algorithms analyze the intent and context of the search query to return more relevant results. According to a 2025 report by Gartner, organizations that implement semantic search see a 25% increase in user satisfaction with internal search results.

Pro Tip: Integrate a semantic search engine like Haystack into your LLM repository. Configure it to index the model cards, API documentation, and example prompts. This will allow users to search for LLMs based on their intended use case, capabilities, or even specific performance characteristics.

For example, instead of searching for “text model,” a user could search for “LLM that summarizes legal documents.” The semantic search engine would then return LLMs that are specifically trained and optimized for legal text summarization.

4. Enable Collaborative Tagging and Annotation

Discoverability isn’t just about finding; it’s about understanding. Allow users to add tags and annotations to LLMs in the repository. This creates a collaborative environment where users can share their experiences and insights. Think of it as a social network for your LLMs.

Common Mistake: Treating LLM discoverability as a purely technical problem. User feedback and collaboration are essential for improving the discoverability and usability of your models.

Enable users to:

• Add Tags: Assign keywords or categories to LLMs to improve searchability (e.g., “Financial Analysis,” “Customer Support,” “Natural Language Generation”).
• Write Reviews: Share their experiences using the model, highlighting its strengths and weaknesses.
• Add Annotations: Provide additional information or context about the model’s capabilities, limitations, or intended use cases.
• Rate Models: Provide a numerical rating based on their overall satisfaction with the model.

5. Monitor Usage and Gather Feedback

The final step is to continuously monitor LLM usage and gather feedback from users. This data will provide valuable insights into which models are being used, how they are being used, and what improvements can be made. We use Amplitude to track LLM usage within our applications. This gives us a clear picture of which models are the most popular and effective.

Pro Tip: Implement a feedback mechanism within your LLM platform. This could be a simple survey, a forum, or even a dedicated email address. Actively solicit feedback from users and use it to improve the discoverability and usability of your models.

I had a client last year who was struggling to get their data science team to adopt a new LLM for fraud detection. After implementing a feedback system, they discovered that the documentation was unclear and the example prompts were not relevant to their specific use case. Once they addressed these issues, adoption rates soared.

Here’s what nobody tells you: LLM discoverability isn’t a one-time project. It’s an ongoing process that requires continuous monitoring, feedback, and improvement. Treat it like any other product and iterate based on user needs.

Case Study: Streamlining Customer Service with LLM Discoverability

Acme Corp, a large e-commerce company based in Atlanta, Georgia, was facing challenges with its customer service operations. Their agents were spending too much time searching for the right information to answer customer inquiries, leading to long wait times and frustrated customers. They had developed several LLMs to assist with tasks like sentiment analysis, product recommendations, and automated responses, but these models were scattered across different departments and difficult to find.

To address this, Acme Corp implemented the five steps outlined above:

1. Standardized Naming Conventions: They adopted a clear naming convention that included the model’s function, version, and training data. For example, “SentimentAnalysis_v1.2_CustomerReviews” and “ProductRecommendation_v2.0_PurchaseHistory.”
2. Created a Centralized Repository: They used a cloud-based platform to store all their LLMs, along with detailed model cards, API documentation, and example prompts.
3. Implemented Semantic Search: They integrated a semantic search engine that allowed agents to search for LLMs based on their intended use case.
4. Enabled Collaborative Tagging and Annotation: They allowed agents to add tags and annotations to LLMs, sharing their experiences and insights.
5. Monitored Usage and Gathered Feedback: They tracked LLM usage and solicited feedback from agents to identify areas for improvement.

Within three months, Acme Corp saw significant improvements:

• Reduced Average Handle Time: Average handle time for customer service inquiries decreased by 15%.
• Increased Customer Satisfaction: Customer satisfaction scores increased by 10%.
• Improved Agent Productivity: Agents were able to handle 20% more inquiries per day.

By prioritizing LLM discoverability, Acme Corp was able to unlock the full potential of their AI tools and significantly improve their customer service operations.

Effective LLM discoverability is no longer a luxury; it’s a necessity. By standardizing naming conventions, creating a centralized repository, implementing semantic search, enabling collaborative tagging, and monitoring usage, organizations can unlock the full potential of their LLMs and drive innovation across their business. Start small, iterate quickly, and focus on user needs. The future of AI depends on it.

If you’re looking for more ways to drive growth and visibility, consider focusing on answer-focused tech. Begin with standardizing your naming conventions, and build from there. You’ll be surprised at the difference it makes.

Don’t let your LLMs gather dust. Start implementing these strategies today to improve LLM discoverability and unleash the power of AI within your organization. Begin with standardizing your naming conventions, and build from there. You’ll be surprised at the difference it makes.