How are companies taking ChatGPT to production with its unpredictability and inaccuracy?

The story was flavoured using GPT-4

[image error]

As we dive into the fascinating world of ChatGPT, many have pondered how companies can take this seemingly unpredictable and inaccurate language model into production. Fear not, for we have unraveled the mysteries of ChatGPT’s abilities and offer you a guide to harnessing its power while maintaining a sense of humor. So sit back, relax, and enjoy this 4-minute read on how to make ChatGPT work for you.

ChatGPT’s abilities can be broadly categorized into three main areas:

Extracting Information/KnowledgeReasoningTaking Actions

Though ChatGPT itself cannot perform real-world actions, worry not! This can be easily achieved by integrating it with a code interpreter.

🔍 Extracting Information: ChatGPT is like the Sherlock Holmes of information extraction. Give it a knowledge base, and it will expertly extract the necessary details, saving you and your users from the dreary task of reading through a mountain of text. While it may occasionally embellish facts or daydream (a.k.a. hallucinate), you can rein in its creative tendencies using temperature settings and specifically prompting it with “Answer only by extraction.” With some fine-tuning, ChatGPT can transform into a highly accurate, production-ready information extractor.

💭 Reasoning: ChatGPT’s reasoning skills can be a bit like a moody teenager — accurate at times, but highly dependent on context. If you’re dealing with mathematical problems, you might need to coax it with a chain of thought prompting and other techniques to make it more reliable. Taking any large language model (LLM) to production without context-specific training is like asking a dog to solve a Rubik’s Cube — not recommended.

Take Tars, for example. When automating customer support for a public sector agency, they set boundaries for ChatGPT. If the model is unsure or lacks the necessary information to reason, it humbly admits, “I am not sure,” or connects the user with a human support agent. As the model learns from past knowledge gaps, it becomes better equipped to assist users in the future, evolving like a Pokémon.

🏃‍♀️ Taking Actions: Allowing AI to take action without supervision can be as problematic as handing a teenager the keys to a Ferrari. Combine that with flawed reasoning, and you’ve got yourself a recipe for disaster. For instance, if a sneaky user requests a refund after consuming a service, your AI shouldn’t comply without proper checks.

To avoid such situations, it’s crucial to define the context and specify the tasks your AI is allowed to perform. Context-specific training is your trusty sidekick here, ensuring your AI produces the results you desire, without causing havoc.

In conclusion, companies serious about taking AI to production don’t simply embrace ChatGPT’s APIs in a bear hug. They invest weeks in training the model within the customer’s use-case context to achieve the automation they seek. So, if you’re ready to embark on your ChatGPT journey, remember to buckle up, configure, train, and enjoy the ride!

[image error]