Problem-solving has been one of the innate capabilities of humans. Ever since our primitive days, when our major challenges in life were not getting eaten by a preying beast to the contemporary times to get something delivered home fast, we have been combining our creativity, logical reasoning, and intelligence to come up with resolutions for conflicts.
Now, as we witness the genesis of AI sentients, we are faced with new challenges with respect to their decision-making capabilities. While the previous decade was all about celebrating the possibilities and potential of AI models and applications, this decade is about going a step further – to question the legitimacy of decisions taken by such models and to deduce the reasoning behind them.
As explainable artificial intelligence (XAI) gains more prominence, this is the moment to discuss a key concept in developing AI models we call Chain-of-Thought Prompting. In this article, we will extensively decode and demystify what this means and simple terms.
What Is Chain-of-Thought Prompting?
When the human mind is poised with a challenge or a complex problem, it naturally tries to break it down into fragments of smaller sequential steps. Driven by logic, the mind establishes connections and simulates cause-and-effect scenarios to strategize the best possible resolution for the challenge.
The process of replicating this in an AI model or system is Chain-of-Thought prompting.
As the name suggests, an AI model generates a series or a chain of logical thoughts (or steps) to approach a query or conflict. Visualize this as giving a turn-by-turn instruction to someone asking for a route to a destination.
This is the predominant technique deployed in OpenAI’s reasoning models. Since they are engineered to think before they generate a response or an answer, they have been able to crack competitive exams taken by humans.
Benefits of Chain-of-Thought Prompting
Anything that is logic-driven yields a significant edge. Similarly, models trained on chain-of-thought prompting offer not just accuracy and relevance but a diverse range of benefits including:
Enhanced problem-solving capabilities, where their importance is critical in fields such as healthcare and finance. LLMs that deploy chain-of-thought prompting better understand explicit and underlying challenges and generate responses after considering distinct probabilities and worst-case scenarios.
Mitigating assumptions and results generated from assumptions because models apply logical and sequential thinking and processing to conclude rather than jumping to conclusions.
Increased versatility as models need not be trained rigorously on a fresh use case as they go by logic and not purpose.
Optimized coherence in tasks involving multi-fold/multi-part answers.
The Anatomy Of Chain-of-Thought Prompting Technique’s Functioning
If you are familiar with the monolithic software architecture, you would know that the entire software application is developed as a single coherent unit. Simplifying such a complex tax arrived with the microservices architecture method that involved the breaking down of software into independent services. This resulted in faster development of products and seamless functionality as well.
CoT prompting in AI is similar, where LLMs are guided through a series of sequential processes of reasoning to generate a response. This is done through:
- Explicit instructions, where models are directly instructed to approach a problem sequentially through straightforward commands.
- Implicit instruction is more subtle and nuanced in its approach. In this, a model is taken through the logic of a similar task and leverages its inference and comprehension capabilities to replicate the logic for its presented problems.
- Demonstrative examples, where a model would lay out step-by-step reasoning and generate incremental insights to solve a problem.
3 Real-world Instances Where CoT Prompting Is Used
Finance Decision Models
| Multimodal CoT In Bots
| Healthcare Service
|
|---|---|---|
| In this highly volatile sector, CoT prompting can be used to understand the potential financial trajectory of a company, conduct risk assessments of credit seekers, and more | Chatbots that are developed and deployed for enterprises demand niche functionalities. They have to showcase abilities in understanding different formats of inputs. CoT prompting works best in such cases, where bots have to combine text and image prompts to generate responses for queries. | From diagnosing patients from healthcare data to generating personalized treatment plans for patients, CoT prompting can complement healthcare goals for clinics and hospitals. |
Example
Customer Query: I noticed a transaction on my account that I don’t recognize, my debit card has been lost, and I want to set up alerts for my account transactions. Can you help me with these issues?
Step 1: Identify and Categorize the Issues
- Unrecognized transaction.
- Lost debit card.
- Setting up transaction alerts.
Step 2: Address the Unrecognized Transaction
Ask for Details: Could you provide the date and amount of the transaction?
- Branch 1: If details are provided:
- Review the transaction. If fraudulent, ask if the customer wants to dispute it.
- Branch 2: If no details:
- Offer to provide a list of recent transactions.
Step 3: Address the Lost Debit Card
Freeze the Card: Recommend immediate freezing.
- Branch 1: If the customer agrees:
- Freeze the card and ask if they want a replacement. Confirm shipping address.
- Branch 2: If the customer declines:
- Advise monitoring the account for unauthorized transactions.
Step 4: Set Up Transaction Alerts
Choose Alert Method: SMS, email, or both?
- Branch 1: If a customer chooses:
- Set alerts for transactions above a specified amount. Ask for the amount.
- Branch 2: If unsure:
- Suggest a default amount (e.g., $50) and confirm.
Step 5: Provide a Summary and Next Steps
- Investigating the unrecognized transaction.
- Freezing the debit card and possibly issuing a replacement.
- Setting up transaction alerts as requested.
Rationale:
This process efficiently addresses multiple customer queries through clear steps and decision branches, ensuring comprehensive solutions.
Limitations of CoT prompting
Chain-of-thought is indeed effective but it’s also subject to the use case it’s applied on and several other factors. There are specific challenges associated with CoT prompting in AI that prevent stakeholders from completely leveraging its potential. Let’s look at the common bottlenecks:
Overcomplicate Simple Tasks
While CoT prompting works best for complex tasks, it can complicate simple tasks and generate wrong responses. For tasks that require no reasoning, direct-answer models work best.
Increased Computational Load
The processing of CoT prompting requires significant computational load and if the technique is deployed on smaller models that are built with limited processing abilities, it may overwhelm them. Consequences of such deployments may include slower response times, poor efficiency, incoherence, and more.
Quality Of AI Prompt Engineering
CoT prompting in AI works under the assumption (or principle) that a specific prompt is well articulated, structured, and clear. If a prompt lacks these factors, CoT prompting loses the ability to grasp the requirement, resulting in the generation of irrelevant sequential steps and ultimately responses.
Reduced At-scale Capabilities
Stakeholders can experience their models struggling if they have to leverage chain-of-thought prompting for massive volumes of datasets or complexities of problems. For tasks involving larger reasoning steps, the technique may slow down response time, making it unfit for applications or use cases that demand real-time response generation.
CoT prompting is a phenomenal technique for optimizing the performance of Large Language Models. If such shortcomings can be addressed and resolved through optimization techniques or workarounds, they can yield incredible results. As tech advances, it’ll be interesting to see how Chain-of-Thought prompting evolves and becomes simpler yet more niche as well.
