Key Takeaways:
- LLM prompt engineering is the process of writing instructions for AI models to inform how they generate content.
- LLM prompt engineering is an important skill for creating effective AI-generated content across multiple mediums.
- In conversational AI video applications, for instance, a well-crafted prompt can mean the difference between a video that feels visually disruptive and one that feels deeply personal.
- Following LLM prompt best practices can help you create better prompts for more coherent, accurate, and authentic results.
Large language models (LLMs) are revolutionizing how people create and interact with AI-generated content. But getting great results isn’t guaranteed—it all starts with giving it the right instructions.
Crafting the right prompt is the key to unlocking the full potential of an LLM. However, like any tool, LLM prompts work best when used correctly. By following a few simple best practices, you can make sure your LLM delivers consistently high-quality outputs—especially when it comes to generating conversational AI videos.
Tavus API offers developers easy access to high-quality artificial intelligence models—without the need for AI or LLM prompting expertise. For those interested in customizing their models, however, Tavus enables integration of OpenAI-compatible LLMs, along with guidance for implementing your custom LLM.
In this guide, we’ll share tips to help you design better LLM prompts. Whether improving an existing AI workflow or starting fresh, these ideas can help you get more from your AI-powered content.
What are LLM Prompts?
LLM prompts are the inputs or instructions large language models use to inform the tone, structure, and content of the outputs or responses they generate. Once a prompt is entered, the LLM analyzes it using the patterns learned during training to generate coherent and contextually relevant, human-like responses.
In most cases, the quality of the prompt determines the quality of the AI’s output: poorly-engineered LLM prompts yield less effective results.
In conversational AI video applications, for example, LLM prompts enable agentic AI—AI that appears autonomous and capable of dynamic interactions. These prompts are like the set directions or broad instructions for creating engaging and dynamic video content.
Importance of Effective LLM Prompt Engineering
As AI becomes increasingly widespread, developers who can create clear, well-crafted prompts and consistently generate high-quality, accurate, and engaging content will have a significant advantage over those who cannot.
In video applications, for example, well-engineered prompts are critical for making conversational AI human-like. These instructions inform the AI’s natural speech patterns, emotional tone, and contextual relevance. How an LLM prompt is engineered can ultimately mean the difference between end-user content that feels disruptive and content that fosters a deeper connection.
With Tavus API, you don’t have to worry about LLM prompting techniques—Tavus handles model training for you. Simply integrate Tavus into your tech stack with an API call, and your end users can generate thousands of videos with just two minutes of training video and their desired scripts.
And for developers who want to customize their LLM, Tavus offers easy custom LLM onboarding guidelines.
Request a free demo today to see Tavus’ LLM in action.
Types of LLM Prompts
Different applications may call for different types of LLM prompts to get the best results. Here are common types of LLM prompts:
- Direct instruction: Prompts that give the AI specific tasks to perform
- Task completion: Prompts that instruct the AI to complete a partially provided script or idea
- Few-shot learning: Prompts that provide a few examples to teach the AI how to respond to similar inputs
- Story continuation: Prompts that instruct the AI to extend a narrative or dialogue
- Question-Answer: Prompts that ask for clear and concise answers to user questions
Conversation LLM Prompt Engineering Best PracticesUse the following best practices to create clear and effective LLM prompts that produce consistently relevant and engaging AI-generated content.
1. Get Straight to the Topic
AI models offer the most relevant responses with focused, actionable instructions. When crafting LLM prompts, provide direct instructions that clarify the task immediately. Tips:
- Start with direct instructions
- Use action verbs
- Avoid filler language
Example: Generate a conversational AI video of a helpful customer support avatar that explains how to integrate our product into an existing CRM platform.
2. Be Clear and Specific
Vague LLM prompts can cause AI to generate unfocused or unexpected outcomes. Providing specific instructions can help improve accuracy and reduce post-production time.
Tip: Clearly define the audience, tone, and desired outcome in your prompt.
Example: Create a conversational AI video of a cheerful onboarding specialist who can guide new users through setting up our software.
3. Provide Context
Without context, LLMs can generate content that just doesn’t align with your goals. Setting the context can help AI models prioritize the right information and produce more relevant outputs.
Tip: Include details like the goal, the target audience, and key challenges.
Example: Generate a conversational AI video of a sales representative addressing common questions about our platform’s video personalization features for SaaS companies.
4. Use Affirmative Statements
Negative instructions can also confuse AI models. Affirmative LLM prompts, on the other hand, which frame instructions positively using “do” statements, can help guide the AI toward more precise and useful outputs.
Tip: Focus on what the AI should include, using clear, actionable instructions.
Example: Generate a conversational AI video of a knowledgeable expert explaining our product’s ability to create multilingual videos for global audiences.
5. Pay Attention to Instruction Formatting
Well-structured prompts, with clear formatting like headings, bullet points, and lists, can improve how the model processes instructions to create cleaner and more organized content.Tips:
- Break tasks into sections
- Separate instructions from examples
Example: Create a conversational AI video for customer service with a friendly and helpful digital representative. The video should contain the following sections:
- Greeting: Welcome users and ask how you can assist.
- Core Task: Answer user questions about integration options.
- Closing: Offer further assistance and a follow-up contact option.
6. Engage With the Model and Answer Questions
Engaging with AI models regularly can help ensure they generate content that aligns with your overall objectives. When interacting with AI, review its responses to identify areas for improvement and to inform how you will write or update prompts in the future.
Tip: Refine initial outputs with follow-up questions or adjustments to the prompt.
Example: Generate a conversational AI video of a support agent responding to questions about video rendering times with step-by-step troubleshooting instructions.
7. Advise the Model on Content and Style
Tailored prompts specifying the intended tone, style, and level of formality can help AI produce more polished and brand-appropriate content that appeals to your target audience.
Tip: State whether the tone should be formal, conversational, or upbeat based on the audience.
Example: Generate a conversational AI video of a friendly virtual sales agent explaining our product’s benefits for small businesses in an approachable tone.
8. Break Down Complex Tasks
Breaking tasks down by splitting inputs into smaller, more manageable steps can help the AI model better understand your instructions and help you refine your LLM prompts for clarity in the future.
Tip: Divide the flow into segments, like greeting, explanation, and follow-up.
Example:
Step 1: Create a conversational AI video introducing our platform.
Step 2: Add a section explaining its personalized video capabilities.
Step 3: Conclude with a call-to-action for scheduling a demo.
9. Try "Tipping" the Model
Adding a motivational element to your prompt—like offering a ”tip”—can help guide the AI toward better responses. Framing your request as “I’ll tip you $200 for the best solution,” for example, can yield higher-quality outputs than smaller tips, like $20.
Tip: Use motivational phrasing or specific goals to subtly steer the models’ focus.
Example: Highlight that our company helps businesses improve customer retention by 20% through personalized video interactions, and I’ll tip you $200 for the best solution.
10. Give Examples
Examples clarify what you expect by providing a standard for the AI to follow. Including sample responses or scenarios in your instructions can help guide the AI with an example of what you’re looking for so it can attempt to replicate it.
Tip: Provide examples that match the format, tone, or style of the content you want to emulate.
Example: Here’s an example of how our company typically starts its onboarding videos: “Welcome! Let me guide you through setting up your first personalized video.” Now, create a similar introductory sequence.
11. Be "Strict"
Loosely defined LLM prompts can elicit off-topic or overly verbose responses from AI. Strict prompts, however, can help prevent it from creating unnecessary content by setting clear boundaries to keep the AI’s output focused and concise.
Tip: Define word limits, sections, tone, and scope to make sure the content meets your needs.
Example: Generate a 90-second conversational AI video in a professional tone, focusing only on our platform’s integration capabilities with CRM systems.
12. Mitigate Bias
Without the proper guidance, AI models can unintentionally produce biased content. Using bias-free prompts that instruct the AI to maintain objectivity and avoid cultural or demographic assumptions, however, can help it create more inclusive and professional outputs.Tips:
- Request neutral language
- Clarify that responses should avoid stereotypes
- Give the model reliable sources
Example: Create conversational AI video avatars for global teams representing a wide spectrum of identities and our company’s commitment to inclusivity and diversity.
13. Use Delimiters to Provide Structure
AI is notorious for misinterpreting and combining unrelated instructions. Using delimiters—like brackets, quotation marks, or colons—to organize your prompt can help the AI separate and process your input more accurately.
Tip: Clearly define sections or examples within your prompt for clarity.
Example: Explain our product’s key features in this order: [Scalability], [Personalization], [Multilingual Capabilities].
14. Repeat Important Phrases
Repetition reinforces the key points so AI can prioritize the most essential information, which can also help it improve the relevance and focus of its output. If your AI model often omits important details in its responses, for instance, try emphasizing their significance by repeating them throughout the prompt.
Tip: Reinforce the key idea by repeating it in different parts of the prompt.
Example: Create a conversational AI video of a representative explaining how our platform automates personalized video creation. The video should start with a greeting and introduction, followed by a detailed overview of how our platform helps companies automate personalized video creation, and conclude with a closing that includes an opportunity for additional Q&A about automating personalized video creation and follow-up opportunities for interested viewers.
Advanced Prompt Engineering Techniques
In some cases, advanced prompt engineering techniques may be necessary for AI to generate more nuanced, contextually accurate responses. These techniques can be especially useful for handling complex queries, guiding logical reasoning, or delivering highly customized outputs:
- Few-Shot Prompting: Provide clear, relevant, and varied examples within the prompt to help train the AI on specific tasks or response styles and generate more accurate outputs.
- Chain-of-Thought Prompts: Instruct the AI to break down its reasoning into clear, step-by-step explanations—before it delivers a final response—for additional clarity into its decisions.
- Tree-of-Thought Prompting: Ask AI models to explore multiple solutions or approaches, possibilities, and outcomes for a well-rounded response.
Fine-Tuning vs. Prompt Engineering
Another important part of effective LLM prompt engineering is fine-tuning. This is the process of retraining an AI model with specific, high-quality data to customize its internal parameters, test, and adapt it for more specialized tasks.
It’s ideal for long-term, specialized use cases and tasks requiring consistent, domain-specific outputs. An open-source LLM model like Meta’s Llama 3 8B, for instance, can be fine-tuned to create highly customized personas for AI-generated conversational videos.Prompt engineering, on the other hand, is creating clear and specific instructions to guide the AI’s behavior without modifying the model itself. It’s best for quick, flexible tasks, like generating personalized conversational AI videos with nothing but a script.
Learn More About LLM Prompts
We’ve got answers to common LLM prompt questions to help you learn to use them effectively.
What is the difference between a system prompt and a user prompt?
A system prompt sets the AI’s behavior and tone across all tasks for a more consistent tone and personality throughout interactions. A user prompt, on the other hand, gives the AI specific instructions for a single task and has more flexible applications.
How can I improve the output for an LLM prompt?
Try these best practices to improve your AI output:
- Use clear, specific language to define the task.
- Provide context like purpose and audience.
- Break complex requests down into smaller steps.
- Test and revise your prompt based on initial outputs.
- Include examples to guide the AI toward your desired outcome.
When should I prompt vs. fine-tune?
Use prompting for quick, flexible tasks that don’t require tons of customization. Choose to fine-tune when you need long-term, consistent outputs.
Craft Effective Conversation LLM Prompts Today
Crafting effective LLM prompts doesn’t have to be complicated. With the right approach and tools, you can design prompts that deliver accurate, meaningful results for your projects.
Tools like Tavus make it easy for developers to add AI-generated video capabilities to their apps, software, or platforms. Whether you’re helping users build personalized customer experiences or dynamic user interactions, Tavus’ APIs can help them create high-quality, engaging videos in no time.
With Tavus, integrating conversational AI video technology into your existing tech stack is easy.
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