Four Principles and Practices to Improve Prompt Engineering Efficacy

The journey of developing ResearchGOAT has been filled with discoveries, challenges, a steep learning curve and lessons learned. These insights aren’t just key takeaways from a product development journey; there are many lessons learned broadly applicable for anyone looking to harness the potential of AI in their qualitative research workflow. Here are four of the most important.

Mandatory Clarity

Working with AI is a bit like explaining directions to someone who’s never visited your town before. The more precise and detailed you are, the less likely they are to get lost. We quickly learned that AI operates on a very literal level. If your instructions or questions are vague or ambiguous, the AI’s output will reflect that confusion. It’s been a lesson in the art of fine-tuning our questions and prompts to achieve more accurate and relevant results. For all its wizardry, AI can’t read minds or fill in blanks with common sense like a human would. Precision in your requests doesn’t simply make the platform better – it’s what’s required to make it work at all.

Context Is Key

Another major takeaway is that context is everything. AI, particularly LLMs, thrives on context. The richer the background information provided, the more nuanced and relevant the AI’s contributions can be. AI doesn’t fill in gaps with shared experience or intuition like a human colleague might. We discovered that the more background and scope we provided, the richer and more nuanced the AI’s contributions became. It’s akin to setting the scene in a novel; the detail draws you in and makes everything clearer. This realization prompted us to approach AI not just as a tool but as a collaborator, armed with as much context as possible for every task. In practice, this means sharing with AI not just what you’re researching but providing a narrative on why it matters, the hypotheses under consideration and the expected significance of potential findings.

Embrace the Iterative Process

The iterative process tests the patience of even the most dedicated prompt engineer. Persistence in refining prompts and interpreting responses from AI form a critical iterative loop. Rarely does the first interaction with AI yield perfect – or sometimes even viable – results. This iterative process is reminiscent of the scientific method where hypotheses are tested, results are analyzed, deviations are studied and hypotheses are refined. It’s also very similar to the process of trial and error inherent in software development with the notable exceptions that the instructions are in English vs. code. Each cycle of interaction with AI provides an opportunity to clarify, expand or redirect its focus. Embrace this process as part of the AI collaboration, recognizing that achieving the desired outcome often requires several (or many) rounds of adjustment. Each attempt is an opportunity to refine and redefine, gradually shaping the outcome closer to what you envisioned. This cycle of trial, feedback and adjustment became a cornerstone of our workflow, teaching us that persistence and iteration are keys to unlocking AI’s potential.

Calibrate Through Rich Examples

Lastly, setting benchmarks with examples turned out to be a crucial strategy. AI, in many ways, learns like we do – from examples. Want better outcomes? Show it what “better” looks like. This involves feeding AI instances of high-quality research questions, robust data analysis or insightful conclusions. These examples serve as benchmarks, guiding the AI in calibrating its performance towards these standards. It highlights an important facet of AI in research: AI doesn’t just need data, it needs direction on what success looks like, drawn from human expertise and experience. You’re the expert, use it to help the AI.

While the journey to developing ResearchGOAT was a deep dive into the practical challenges and opportunities of leveraging the current capabilities of generative AI in qualitative research, the lessons extracted have implications beyond the platform across different domains. Whether you’re an academic, a market researcher or simply a curious person with questions, the fusion of human intuition with AI’s analytical prowess holds the promise of not just streamlining research processes but elevating the insights we derive from our quests for keener perspicacity and understanding.

Ready to put
ResearchGOAT to Pasture.
Harness the herd.