The rise of Large Language Models (LLMs) like BERT, Claude, GPT, and Orca has created unprecedented possibilities in the realm of generative AI. These powerful models can create human-quality text, translate languages, answer questions, create content, and more. However, unleashing the full potential requires a specific skill referred to as LLM prompt engineering.
This guide explains LLM prompt engineering by discussing the definition, importance, working principles, benefits, and use applications. The guide concludes with an explanation of several tips and tricks to get the most out of an LLM.
What is LLM prompt engineering?
Imagine an LLM as an incredibly well-informed blank slate that is eager to learn more and respond to any inquiry. The blank slate will need guidance to create the desired response. This is where prompt engineering comes into play. LLM prompt engineering is the process of formulating instructions for an LLM that will achieve the desired results. However, prompt engineering extends beyond simply asking the right questions to get the best answer. Prompt engineering is integral to all interactions with LLMs, even development.
Why is LLM prompt engineering important?
For an LLM trained on vast and diverse datasets, the world is full of unlimited possibilities. Much like a toddler who is just beginning to learn daily tasks, boundaries, relationships, and societal norms, LLMs need clear instructions to produce the desired behavior. Prompt engineering is important because it allows LLM users to:
- Refine the behavior of the LLM through precise prompts that focus the LLM on specific aspects of the task at hand.
- Improve the accuracy and coherence of the LLM by providing clear instructions and contextual information.
- Unlock the creative potential of LLMs through storytelling, content creation, and artistic expression.
- Reduce the bias of the LLM by training the model to provide ethical and fair outputs.
How does LLM prompt engineering work?
In the past, if someone wanted to work with an ML model, they had to have extensive knowledge of datasets, statistics, and modeling techniques. Prompt engineering makes it possible for anyone to use plain language to interact directly with an LLM. In prompt engineering, various elements come together to shape LLM's understanding and output. These elements include:
- Context, such as relevant background information about the topic, task, or situation.
- Instructions in the form of clear and simple directions for the LLM to follow.
- Examples of the desired outputs to teach the LLM the objective.
- Guidelines for the tone, style, length, and format guidelines of the output.
While this list may seem intimidating at first, it is important to note that prompt engineering can be and often should be, an iterative process. The first prompt must be simple and direct. After the initial prompt is given and the output is received, the user can refine the prompt with additional instructions or clarifications. Through several iterations, the LLM can learn and eventually achieve the desired results.
Key Benefits of LLM Prompt Engineering
For those who want to make the most of LLMs, prompt engineering is a game-changer, and here are the top three reasons why:
- Boosts the Capacity of LLMs - Prompt engineering allows the user to teach the LLM to reason, decipher patterns, and answer questions. Just like teaching a child to ride a bike, LLMs receive instruction and support to unlock their abilities and refine their skills.
- Improves Understanding of LLMs - While LLMs hold incredible potential, their inner workings can seem quite opaque. Prompt engineering helps users increase their understanding of LLMs' capabilities and limitations, building trust and transparency along the way.
- Improves the Safety of LLMs - LLMs are powerful tools for communication, but they can also be susceptible to misinformation and misuse. Prompt engineering is a valuable LLM teaching tool that can provide vital instruction on boundaries and reason. Prompt engineering also allows users to guide LLMs toward fair and ethical responses.
When can LLM prompt engineering be used?
The possible applications for LLM prompt engineering are virtually unlimited and can include:
- Content creation
- Creative writing
- Question answering
- Code generation
- Data summarization
- Personalized recommendations
- Language translation
- Building AI companions and assistants
- Bridging the gap between humans and machines
- Enhancing scientific discovery and innovation
9 LLM Prompt Engineering Tips and Tricks
Ask any parent, teacher, or caregiver familiar with prompt engineering, and they will likely agree that prompt engineering is much like talking to a toddler whose world is full of unlimited possibilities. If you tell a toddler who is eager to please, "don't put your trash on the floor," they could take that to mean they can put anything they consider trash anywhere except the floor. The "trash" could end up in the refrigerator, laundry hamper, pantry, dresser drawer, or toy box or stay in their hand straight through bedtime.
These humorous toddler behaviors are much like LLM "hallucinations," but for LLMs, the results are not always amusing or endearing. In fact, hallucination outputs can be concerning and even lead to grave consequences. When the world is full of possibilities, providing clear, actionable instructions is the only way to get a specific result. With the right instructions, the toddler and the LLM both learn the expected behaviors over time.
Here are nine prompt engineering tips to get the best results from an LLM.
1. Start Simple
Avoid adding too much complexity at the beginning. Start with simple prompts and then add more information and context in subsequent prompts. In this way, prompting is an iterative process in which the prompt is further developed along the way. By starting simple, there is plenty of room for experimentation and practice to achieve optimal results.
2. Be Clear
It is best for prompt language to be free from any jargon. Stick to a simple vocabulary and focus on providing direct instructions. Try to avoid language that OpenAI refers to as "fluffy descriptions." Any unnecessary text may distract the LLM from the task at hand.
3. Be Specific
In an interaction of prompts, provide the model with everything it needs to know to give a response. In the toddler example from above, it is likely much more effective to name the item they are holding, walk with them to the trash can, show them how to throw it in, and then celebrate the success. In terms of LLM, this approach involves adding in descriptive and contextual information that illustrates the desired outcome. In some instances, this degree of specificity may end up closely resembling storytelling. Detail the desired context, outcome, length, format, and style. Explain what happens before and after a situation. Describe the stakeholders involved. These steps may seem extensive or contradictory to the first two steps, but the more thoroughly the stage is set, the more the model can understand the parameters.
4. Consider the Structure
A giant, uninterrupted block of text is difficult for both humans and LLMs to take in. Punctuation marks and paragraph styles have a crucial role for human readers and also for LLMs. Using bullet points, quotation marks, and line breaks can help the model understand the text better and prevent the possibility that something might be taken out of context.
5. Focus on the "Do's" not the "Don'ts"
When the world is full of possibilities, crossing only one or two things off the list of available options is not very helpful. Even if a few options are made unavailable, essentially, an unlimited number of options still remain.
Back to the toddler example–if an adult is with a child in an environment where they do not want the child to touch objects nearby because they are breakable, dirty, or off-limits, it is not very helpful or effective to simply instruct the child, "don't touch anything." Chances are, this instruction will inspire their desire to touch everything around them as curiosity gets the best of them, and they wonder where to put their hands. The more effective alternative is to playfully instruct them to put their hands on their hands or in their pockets. This instruction gives them a clear and attainable task to accomplish.
Like toddlers, LLMs respond more positively to "do's" than "don'ts." By providing finite instructions, the LLM can learn the desired behavior without any confusion, distraction, or mystery.
6. Use Leading Words
Now, it is time to explore prompts that go beyond providing instructions for behaviors and focus on teaching the model to reason. Leading words are useful for guiding the model to a more effective approach to problem-solving. Nudge the model towards a specific format by writing specific words at the end of a prompt. For example, if the user wants the model to respond by writing in Python, they can add "import" to the end of the prompt. Similarly, by providing the prompt "think step by step," the model is forced to break down the solution into steps rather than just throw out one big guess.
7. Use Few Shot Prompting
To use few shot prompting, it is important to first understand zero shot prompting. Zero shot prompting consists of just one instruction and one request. However, zero shot programming does not always work. It typically only works if the model already understands the concept accurately. When the model is unfamiliar with the concept at hand, few shot prompting can help the model learn the desired pattern. Few shot prompting involves providing the model with examples to explain the concept.
8. Use Chain of Thought Prompting
When few shot prompting does not achieve the desired results, chain of thought (CoT) prompting is the next logical step. CoT involves providing the LLM with an initial question then following that up with a series of natural language reasoning steps that lead to the answer. Essentially, CoT prompting requires the user to break down a big task into smaller chunks that follow a logical progression. While somewhat similar to few shot prompting, CoT prompting utilizes linear steps to teach reasoning and encourage the LLM to explain its reasoning.
9. Use Tree-of-Thought Prompting
Tree-of-Thought prompting is an emergent approach that is still undergoing research to understand its effectiveness. As the next step beyond CoT, tree-of-thought prompting mirrors organizational decision-making processes involving multiple stakeholders. It tries to account for many different thought processes and approaches to a problem by inviting the LLM to imagine multiple experts are answering the question in small steps, sharing insights with the group after each step, and taking corrective action if they realize they are wrong.
Encora's Prompt Engineering
Encora has invested heavily in prompt engineering, developing capabilities that validate the effectiveness and efficiency impacts of Generative AI across the digital engineering software development lifecycle. Encora is prepared to demonstrate the efficacy of Generative AI in client-specific value chains through the fine-tuning of LLMs based on nuanced client data models, making use of validated POCs for scale.
We are working closely with Hyperscalers to deliver Generative AI impacts across value chains in industries we service, including HiTech, Banking, Financial Services and Insurance, Healthcare and Life Sciences, Travel, Transportation and Logistics, Retail & CPG, Telecom and Media, Energy & Utilities, Automotive and others!
Fast-growing tech companies partner with Encora to outsource product development and drive growth. We are deeply expert in the various disciplines, tools, and technologies that power the emerging economy, and this is one of the primary reasons that clients choose Encora over the many strategic alternatives that they have.
Contact us to learn more about LLM prompt engineering.
