Prompt engineering helps in extracting precise information from large language models (LLMs) like Gemini or ChatGPT-4. Here’s a quick guide to the most useful four advanced methods in prompt engineering. We’re going to look at each of the four techniques along with an example AI prompt that will hopefully help you better understand each one.
Retrieval-Augmented Generation (RAG)
RAG combines domain-specific knowledge with the broad data a model has been trained on. It uses a retriever component to fetch relevant information from a specific knowledge base, which then informs the responses generated by the AI.
This method ensures that the AI’s responses are not only based on general internet data, which can be inaccurate or outdated but are augmented with up-to-date, specific information from reliable sources.
Example Prompt: “What are the latest FDA guidelines on the use of artificial intelligence in medical diagnostics?
This prompt would effectively direct the AI to pull the most recent and relevant data from specific regulatory databases or trusted sources, rather than generating an answer based solely on potentially outdated training data.
Chain of Thought (CoT)
CoT involves breaking down a query into simpler, logical steps and having the model tackle each step sequentially. This method guides the AI in a structured thought process, leading to a more reasoned and accurate output.
By simulating a step-by-step problem-solving approach, CoT helps the AI to produce explanations and solutions that are logical and easy to follow, making complex information more accessible.
Example Prompt: “To calculate the total environmental impact of manufacturing a smartphone, start by assessing the CO2 emissions from material extraction, then consider the energy consumption during production, and finally sum these figures.”
This prompt breaks down the complex task into manageable steps, guiding the AI to consider all relevant factors and synthesize them into a final comprehensive answer.
Retrieval Augmented Comprehension and Thinking (ReACT)
ReACT takes the capabilities of RAG further by enabling the model not only to retrieve information but also to act by fetching data from external sources when necessary. This method is particularly useful when the internal knowledge base does not have all the needed information.
ReACT allows for dynamic responses that utilize a broader spectrum of information, combining both private and public data sources to deliver comprehensive and accurate answers.
Example Prompt: “Compare the economic growth rates of countries A and B using the latest GDP data. If data for the current year is missing, retrieve it from the global economic database.”
This prompt not only asks the AI to perform a comparison but also to act by fetching missing data from specified external sources, ensuring that the response is based on the most complete information available.
Directional Stimulus Prompting (DSP)
DSP guides the AI to focus on specific aspects of a query, providing ‘directional stimuli’ that help refine the responses. It’s like giving hints to the AI on what details to focus on, ensuring that the output is not just general but tailored to specific needs.
This technique is crucial when you need detailed information on particular segments of a broader topic, allowing for granularity in the responses without overwhelming the user with irrelevant data.
Example Prompt: “Explain how blockchain technology can enhance supply chain transparency, focusing specifically on traceability and fraud prevention aspects.”
By specifying the focus areas, the prompt directs the AI to target its response to include specific details about traceability and fraud prevention, providing a tailored and relevant answer.
Combine Techniques to Enhance Results
While each of these methods can be powerful on its own, combining them can lead to even more effective interactions with AI. For instance, starting with RAG to ground the content in reliable domain knowledge, and then applying CoT or ReACT depending on whether the query requires step-by-step reasoning or accessing external information. DSP can be integrated whenever there is a need to drill down into specifics.
Think of these techniques as tools to create an interwoven framework for advanced AI communication.
Combined Prompt Example: “To assess the impact of recent tax reforms on small businesses in the tech sector, start by retrieving the latest tax legislation from the national business registry. Break down the main changes into categories such as tax rates, deductions, and credits. For any updates not covered in the internal database, consult the most recent IRS public statements. Summarize how these changes could affect operational costs and investment in new technologies.”
This prompt combines the following:
- RAG: It starts with a retrieval request for specific legislative documents.
- CoT: It breaks the query into smaller, logical segments (tax rates, deductions, credits).
- ReACT: It instructs the AI to look for additional external resources if the internal data is incomplete.
- DSP: It directs the focus to operational costs and technological investments, specific aspects of the broader topic of tax reforms.
Using these combined techniques, the masterful prompt ensures that the AI gives a comprehensive, well-structured, and highly detailed response that is directly relevant to the user’s needs. This kind of sophisticated prompt engineering can greatly enhance the effectiveness of AI-driven analyses and decision-making processes.
Above all else, if you get frustrated with AI because you’re not getting the results you expect – look at your prompt. AI can only do what you tell it to do so fine-tune your prompt and stack the techniques from this article and try it again!
These prompt engineering techniques aren’t just academic; they’re practically applicable in industries ranging from finance to healthcare, where precision and reliability of information are paramount. If you plan on leveraging the full potential of AI, you need to develop a mastery of these methods to guarantee accurate and contextually relevant outputs. Or sign up with AkzisAI and use our handy expertly crafted templates.
