Effectively Leverage a LLM For Your Business Applications
You’ve likely heard the word “hallucination” used to describe responses from large language model (LLM) applications, such as ChatGPT. In comment threads, blogs, and screenshots, users have shared their experience with the occasional incorrect or downright strange information that these models provide.
For businesses that rely on AI models (also known as deep learning models) for customer service and other client-facing priorities, these hallucinations can be highly problematic. Take the example of Air Canada, whose chatbot promised a passenger that they’d receive a discount which didn’t exist and that the airline ultimately had to honor.
Another challenge businesses experience is prompt injection. Prompt injection is a form of hacking, in which a bad actor attempts to override the model’s prior training for the purpose of uncovering proprietary information. In one famous example, a Stanford student was able to convince the Microsoft Bing chatbot Sydney to “ignore previous instructions” and describe secret information that OpenAI or Microsoft developers used to train the model.
Those with expertise in AI understand that the reliability of today’s best large language models is quite useful. However, this comes with one caveat: your model’s results largely depend on how you prompt it. Businesses should consider tapping the support of deep learning and machine learning model developers to refine their enterprise use of a LLM to reduce hallucinations and prompt injection.
What Is a Prompt?
A prompt is a set of instructions or questions that a model developer provides, and which an LLM receives as a command to execute a certain task, such as information retrieval. AI developers typically use the words “input” to describe a prompt and “output” to describe the results.
LLM output is based on its initial knowledge base and training, although its learning doesn’t have to end there. To elicit the best results from the model your business intends to use, you can engineer how your model responds to user inputs through an interface.
What Is Prompt Engineering?
Prompt engineering is a series of methods developers use to ensure that the LLM’s output provides helpful, risk-free responses for users. These techniques provide more certainty for businesses who wish to rely on a LLM for a production-ready environment.
Much like coding, effective prompt engineering has its own language. Some application programming interfaces (APIs) for LLMs use Python, whereas others are even more user friendly, such as OpenAI’s Playground.
Prompt Engineering Techniques
Data scientists, engineers, and developers have determined that prompt engineering is one of most effective ways to guard your use case against hallucination and prompt injection. Although model development and coding are essential parts of deep learning model creation, prompt engineering has found its place as an important part of the AI engineering ecosystem. Proponents of prompt engineering tout these benefits:
Reduced expenses, as prompt engineering is less costly than comprehensive tuning
Accelerated model performance testing during the prototyping phase
The opportunity to experiment with how the model responds
We curated these prompt engineering best practices to demonstrate a variety of approaches that can help LLMs deliver better responses:
Few-shot prompting: Unlike zero-shot prompting, in which the model is expected to use its general training to provide the answer, few-shot prompting requires the engineer to provide examples of what it expects from a certain type of response. For example, if you want the model to determine a specific tone or mood, you can prompt the model with several examples of statements and an expected sentiment.
Tree-of-thought: Precise, thorough answers require complex prompting. A tree-of-thought is an example of prompting that breaks an input down into roots, branches, and sub-branches to deconstruct the user’s request.
Chain-of-thought: Similar to tree-of-thought prompting, this technique provides steps for the model to follow, which is useful for discursive logic, mathematical reasoning, providing instructions, or organizing comprehensive answers to requests.
Function calling: This technique makes it clear to the model that you expect a certain function when the user names it. Reinforcing a particular verb during prompt training, such as “translate” or “calculate” will yield more accurate results.
Consult Machine Learning and Deep Learning Engineers to Elevate Your AI Capabilities
Businesses today can significantly benefit from prompt engineering to ensure that their LLM use case is highly reliable for users. Although businesses with failed LLM applications have tried to put the onus for failure on the model, responsibility ultimately rests with the company and the team they’ve chosen for AI development.
A leading AI or deep learning consulting team can provide assistance by fine-tuning your model with prompt engineering to help you manage risk and deliver an exceptional product. Your partnership with a team that is experienced with deep learning and machine learning models can also provide support for another highly effective solution to get the most from an LLM: retrieval-augmented generation (RAG). RAGs help businesses create a ChatGPT-like solution that draws on proprietary internal information and data to generate responses.
Find out more about custom RAG solutions from an expert team that can optimize your AI model at any stage.
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