Mastering advanced prompt engineering: Part 1

In the rapidly evolving field of artificial intelligence (AI), prompt engineering has emerged as a critical skill for getting the best results from large language models (LLMs). As these models become increasingly advanced, the techniques we use to interact with them must also evolve. This blog post dives deep into advanced reasoning techniques that push the boundaries of what’s possible with prompt engineering.

In this blog, we’ll explore a wide range of techniques, starting with foundational methods like chain-of-thought (CoT) prompting and its advanced variations: Zero-shot CoT (ZS-CoT), automatic chain-of-thought (Auto-CoT), self-consistency CoT (CoT-SC), and tree of thoughts (ToT) prompting.

In the second part of the blog series, “Mastering advanced prompt engineering: Part 2,” we’ll explore cutting-edge approaches like graph-of-thoughts (GoT) and program-of-thoughts (PoT) prompting. Each method offers unique advantages and applications, making AI essential for complex problem-solving and decision-making tasks.

What is prompt engineering?#

Prompt engineering is the process of designing and refining the inputs, or prompts, given to a language model to achieve desired outputs. It involves crafting specific queries, instructions, or examples to guide the model in generating relevant and accurate responses.

While prompts can be as simple and straightforward as a brief question asked by a 5-year-old, we can enhance it with more information to get the desired response from the LLM. Here are some key aspects of prompt engineering:

• Clarity and precision: Ensuring that prompts are clear and precise helps the model understand the task or question correctly. Ambiguous or vague prompts can lead to irrelevant or inaccurate responses.

• Context provision: Providing sufficient context within the prompt can help the model generate more informed and contextually appropriate answers. This may include background information, specific constraints, or desired response formats.

• Examples and templates: Using examples or templates in prompts can guide the model to produce outputs in a specific style or format. This is particularly useful for text generation, translation, or summarization tasks.

• Iterative refinement: Prompt engineering often involves iterative refinement, where prompts are adjusted based on the model’s responses to achieve better results. This process can include changing wording, adding or removing context, or rephrasing questions.

• Task-specific prompts: Different tasks may require different types of prompts. For example, a prompt for a chatbot might differ significantly from a prompt for a summarization task. Tailoring prompts to the specific task can improve performance.

• Handling limitations: Understanding the language model’s limitations and designing prompts that work around them can enhance the quality of responses. This might involve avoiding ambiguous phrasing or providing explicit instructions to mitigate known issues with the model.

The evolution of prompt engineering#

Before we dive into specific techniques, it’s important to understand the context in which these advanced prompt engineering methods have developed. Traditional prompt engineering focused on crafting clear, concise instructions to elicit desired responses from AI models. However, as the complexity of tasks increased—such as requiring multi-step reasoning, deeper contextual understanding, and the ability to handle cross-domain knowledge and ambiguity—it became clear that more advanced approaches were needed to achieve higher accuracy and performance.

The following illustration represents the evolution of prompt engineering techniques from manual prompting to advanced prompting:

Advanced prompt engineering techniques#

Prompt engineering has evolved because large language models (LLMs) are now expected to handle more complex tasks. New techniques in prompt engineering help these models reason better and solve problems more accurately by structuring prompts in ways that encourage more advanced, multi-step reasoning processes. These techniques can guide AI models to tackle complex tasks more accurately and transparently.

Advanced prompt engineering generally fall into several categories, each with its own strengths and ideal use cases. These techniques range from linear, step-by-step approaches to more complex, interconnected reasoning frameworks. By understanding and applying these methods, prompt engineers can significantly improve the performance of AI models on a wide range of tasks, from mathematical problem-solving to complex decision-making scenarios.

We’ll explore some of the most powerful and widely used advanced prompt engineering techniques, starting with the foundational chain-of-thought prompting and its variations.

Chain-of-thought (CoT) prompting#

Chain-of-thought (CoT) promptingWei, Jason, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V. Le, and Denny Zhou. "Chain-of-thought prompting elicits reasoning in large language models." Advances in neural information processing systems 35 (2022): 24824-24837 guides LLMs to solve complex tasks by breaking them into step-by-step logical processes, improving accuracy, and making reasoning more transparent.

CoT prompting example#

Let’s consider a simple mathematics problem:

To test the prompt in a chatbot, you’ll likely get a response similar to the one below. Click the “Show Response” to see the full details.

To better understand CoT prompting, let’s look at its advantages and limitations in the table below:

Zero-shot CoT (ZS-CoT)#

To address the scalability issue of CoT prompting, researchers developed ZS-CoT, which uses the prompt “let’s think step by step” to encourage step-by-step reasoning without providing explicit examples.

ZS-CoT prompting example#

To test the prompt in a chatbot, you’ll likely get a response similar to the one below. Click the “Show Response” to see the full details.

To clearly see the benefits and drawbacks of ZS-CoT, refer to the table below:

Automatic Chain-of-Thought (Auto-CoT)#

Automatic chain-of-thought (Auto-CoT)Zhang, Zhuosheng, Aston Zhang, Mu Li, and Alex Smola. "Automatic chain of thought prompting in large language models." arXiv preprint arXiv:2210.03493 (2022). is an advanced technique that addresses the limitations of both traditional CoT and ZS-CoT by automating the process of generating reasoning chains for diverse questions.

Overview of Auto-CoT workflow#

The workflow consists of several key steps:

1. Question clustering

2. Demo construction

3. Sampling by selection criteria

4. Auto demos, one by one

5. In-context reasoning for test questions

Let’s explore each of these steps in detail.

1. Question clustering#

The process begins with a diverse set of questions. These questions are clustered based on their characteristics and complexity, which helps ensure that the final set of examples covers many problem types.

Example:

• Cluster 1 might contain arithmetic problems

• Cluster 2 might contain word problems involving time calculations

• Cluster k might contain logic puzzles or complex multi-step problems

2. Demo construction#

After clustering, the system selects representative questions from each cluster. For each selected question, the large language model (LLM) is prompted to generate a reasoning chain using the “let’s think step by step” approach.

Example:

3. Sampling by selection criteria#

From the constructed demos, a subset is selected based on certain criteria. This ensures that the final set of examples is diverse and representative of different problem types and reasoning strategies.

4. Auto demos one by one#

The selected demos are then used as examples for the LLM to learn from. These automatically generated reasoning chains guide the model in approaching similar problems.

Example:

5. In-context reasoning for test questions#

Finally, when presented with a new test question, the LLM uses the auto-generated demos as context to reason through the problem.

Example test question:

To understand the strengths and challenges of Auto-CoT, refer to the table below:

Self-consistency with chain-of-thought (CoT-SC)#

Self-consistency (CoT-SC)Wang, Xuezhi, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. "Self-consistency improves chain of thought reasoning in language models." arXiv preprint arXiv:2203.11171 (2022). is an advanced technique that enhances CoT prompting for language models. It improves accuracy and reliability by using multiple reasoning paths for a single problem and then aggregating the results to produce a more robust answer.

How CoT-SC works#

The self-consistency method consists of three key steps:

1. CoT prompting: The process begins with a standard CoT prompt, asking the model to explain its reasoning step-by-step.

2. Sampling diverse reasoning paths: Instead of using a “greedy decode” approach, the method samples from the language model’s decoder to generate a diverse set of reasoning paths for the same problem.

3. Marginalizing and aggregating: The final step involves marginalizing out the reasoning paths and aggregating the results by choosing the most consistent answer in the final answer set.

This approach replaces the single-path strategy of traditional CoT with a method that explores and utilizes multiple reasoning paths.

Example#

Let’s walk through a comprehensive example to illustrate the self-consistency technique:

We’ll generate three chains of thought:

Analysis and aggregation:

• Two chains (1 and 3) arrived at $53.20

• One chain (2) produced $52

Final answer: $53.20 (most consistent result)

Confidence: Moderate (2 out of 3 chains agree)

Implementation strategies#

To effectively implement self-consistency in your prompts, consider the following strategies:

• CoT prompt design: Craft initial prompts that encourage step-by-step reasoning.

• Sampling setup: Configure the language model’s decoder to sample multiple diverse reasoning paths instead of using greedy decoding.

• Diversity encouragement: Include instructions or techniques to ensure variety in the generated reasoning paths.

• Answer extraction: Implement a system that automatically extracts final answers from each chain.

• Consistency analysis: Develop an algorithm to compare answers and identify the most consistent one across all generated paths.

• Scalability: For complex problems, increase the number of sampled reasoning paths, balancing accuracy and computational cost.

Benefits and challenges of self-consistency#

Self-consistency in prompting offers several benefits over traditional CoT methods but also comes with certain challenges. Below is a table summarizing the key benefits and challenges associated with implementing self-consistency:

Tree-of-thoughts (ToT) prompting#

The tree-of-thoughts (ToT)Yao, Shunyu, Dian Yu, Jeffrey Zhao, Izhak Shafran, Tom Griffiths, Yuan Cao, and Karthik Narasimhan. "Tree of thoughts: Deliberate problem solving with large language models." Advances in Neural Information Processing Systems 36 (2024). technique significantly advances prompt engineering for LLMs. It builds upon and extends previous methods like input-output prompting (IO)Input-output prompting (IO) is a technique that involves providing an LLM with a specific input and expecting a corresponding output, serving as a foundational method in prompt engineering to guide the LLM towards producing desired outcomes., CoT, and CoT-SC, offering a more advanced approach to problem-solving. ToT allows language models to explore multiple reasoning paths simultaneously, providing more comprehensive and straightforward solutions.

Core principles of ToT#

The ToT approach is built on several key principles that differentiate it from other prompting techniques. These principles work together to create a powerful framework for complex problem-solving:

• Branching structure: ToT creates a tree-like structure of thoughts, allowing for the exploration of multiple reasoning paths simultaneously.

• Depth and breadth exploration: The technique enables both deep dives into specific lines of thought and broad exploration of various possibilities.

• Dynamic evaluation: Thoughts are continuously evaluated at each level, prioritizing more promising paths.

• Adaptive reasoning: The model can backtrack and explore alternative branches if a chosen path proves unfruitful.

• State maintenance: Each node in the tree maintains a “state,” allowing for coherent and context-aware decision-making.

How ToT works#

The ToT technique follows a structured workflow that allows for the systematic exploration of ideas. Here’s a step-by-step breakdown of the process:

1. Initial thought generation#

The model begins by generating multiple initial thoughts or approaches to the given problem. This stage sets the foundation for the branching structure that follows.

2. Thought expansion#

After generating initial thoughts, each one is expanded into multiple sub-thoughts, creating branches. This expansion allows for a more detailed exploration of each approach.

3. Evaluation#

At each level of the tree, thoughts are evaluated based on their potential to lead to a solution. This evaluation process is crucial for determining which paths are worth pursuing further.

4. Path selection#

Based on the evaluation, more promising paths are selected for further exploration. Less promising paths might be pruned or deprioritized, allowing the model to focus its resources on the most potential solutions.

5. Iteration and depth#

The process of expansion, evaluation, and selection continues, potentially going several levels deep. Each level adds more specificity and detail to the reasoning process, allowing for increasingly refined solutions.

6. Backtracking#

If a chosen path leads to a dead end or unsatisfactory result, the model can return to a previous node and explore alternative branches. This ability to backtrack ensures that the model doesn’t get stuck in unproductive lines of reasoning.

7. Final output generation#

The final output is derived from the most successful path(s) through the tree. This output often includes not just the answer, but the reasoning process that led to it, providing a comprehensive solution to the problem.

Example: City planning problem#

To illustrate the power and flexibility of the ToT technique, let’s apply it to a complex city planning problem:

The following prompt uses the ToT prompting technique for the problem mentioned above:

In the above example, we can witness how ToT can be applied to the city planning problem.

Benefits and challenges of ToT#

The ToT approach offers numerous benefits over traditional prompting techniques, making it a powerful tool for enhancing problem-solving and reasoning in complex tasks. However, implementing ToT comes with its own challenges that must be carefully managed to fully realize its potential.

Conclusion#

Mastering prompt engineering is essential for anyone looking to harness the full potential of advanced LLMs. This blog has explored foundational techniques such as CoT prompting and its advanced variations like ZS-CoT, Auto-CoT, and CoT-SC. We’ve also dived into advanced strategies like ToT prompting, which structures reasoning processes in a hierarchical manner, enabling models to tackle more complex problems with greater depth. As AI evolves, these advanced reasoning techniques will become increasingly crucial for driving innovation in problem-solving and decision-making across various domains.

Next steps#

Ready to dive deeper into the world of prompt engineering? Here are two exciting resources to get you started:

1. Become a Prompt Engineer

2. Introduction to Prompt Engineering with Llama 3