Generic vs Purpose-built LLM – How to choose for your organization

AI is making waves in Qualitative Research, especially with the rise of Large Language Models (LLMs) that can sift through interviews, focus groups, and open-ended responses in seconds. Custodians of Consumer Insights at research-buying organizations probably ask themselves, from time to time: Should I go with a generic LLM like ChatGPT/ Claude/ Gemini/ CoPilot, or choose a purpose-built solution designed specifically for my organization’s research requirements?

It’s not just a tech decision. Instead, it’s about how the Consumer Insights function works, what custodians value in insights they consume, and how much control they want over the data their studies collect. In this post, we’ll break down the pros and cons to help you make the right call. When deciding between using a Generic LLM and a purpose-built solution for Qualitative Research, consider these guiding questions:

What are your privacy and compliance requirements?

If you handle sensitive data and have strict privacy rules, lean towards Purpose-built solutions that give you more control​. If you do use a generic LLM via API, make sure to opt out of data retention and choose anonymized inputs. Your legal and IT teams might have a say in this decision.

How specialized is your domain or research type?

If your research involves highly technical or domain-specific language (e.g., medical terms, specialized product jargon), a Purpose-built (or fine-tuned) model will likely perform better and with fewer misunderstandings. Generic LLMs are improving, but they may require a lot of prompt engineering to handle niche topics. On the other hand, if your topics are very general consumer issues and you don’t need deep domain knowledge, a generic LLM might suffice for summaries and basic theme extraction.

What’s the volume and frequency of analysis?

For large scale, repeated research (say you analyze hundreds of interviews a month), consistency and efficiency are paramount. Investing in a robust Purpose-built platform could save significant man-hours and ensure standardized output, every time. For occasional or small-scale projects, you might be fine using Generic AI, on a case-by-case basis. It can even be a cost question: if it’s not a regular need, you may not want to pay for a subscription tool and instead just use an API when needed.

Do you have the capability to build and maintain your own solution?

Some organizations with strong Data Science teams might choose a middle path: use Generic LLM APIs; yet develop custom prompts, validations and maybe fine-tune on internal data… essentially, creating an internal purpose-built workflow. This can yield great results tuned to your exact needs, but requires commitment to maintain; as models evolve. If you lack that in-house capability, partnering with a vendor who offers a ready-made solution is the best choice to make.

What’s your budget?

Budget is always a factor. Generic LLMs via API are relatively cheap per use. Even if you scale these, the costs are often lower than Enterprise software seats. Purpose-built solutions range from affordable SaaS tools, to pricey Enterprise contracts. However, consider the total cost vs benefit: a tool that costs $X may save your team Y hours of manual work. The question to ask then is: Is Y hours of researcher time worth more than $X? Often, reducing manual drudgery is worth a lot because it frees your researchers to do higher-value work (like interpreting results, strategy, etc.). If you’re a startup with a lone researcher, you use generic AI tactically, thus accepting to use more manual effort (to save money). If you’re a larger organization, where researcher hours are expensive and in short supply, investing in a tool that cuts those hours down is likely a good trade-off.

How important is control and transparency of the analysis?

If you need to fully control every step of analysis for methodological reasons, you might prefer to use a Generic LLM in a very controlled way; or use a tool that allows customization of prompts, or show you how it arrived at an insight. If you’re comfortable treating the AI as a collaborator, Purpose-built solutions can handle the grunt work and you focus on vetting the output. But ensure the tool provides ways to trace and verify outputs. If a vendor cannot explain in reasonable terms how their AI arrives at results, consider it a red flag.

Pilot and compare, if possible: It doesn’t have to be a blind choice. You can pilot using a Generic LLM on a project and also trial a Purpose-built tool on the same project; then compare the outcomes. Ask yourself:

1. Did the specialized tool find insights you missed?
2. Was it faster or more convenient?
3. Or was ChatGPT’s output surprisingly good with the right prompt?
4. Evaluate quality, speed, ease of use
5. Evaluate how much post-processing was needed, in each case

Conducting such experiments on a small scale, can inform your bigger decision. For example, you might find that ChatGPT can draft decent summaries, but a tool like flowres not only summarized but also organized findings by theme and linked evidence, saving you a lot more time in making a client-ready report.

In many cases, organizations end up with a hybrid approach: using a specialized platform for the core analysis (for reliability and integration reasons), and using Generic AI for auxiliary tasks (like brainstorming questions or quickly checking something outside the platform’s scope). That’s perfectly fine – you don’t have to choose only one and forsake the other entirely.

Finally, remember that AI is a tool to augment human researchers’ talent, not to replace it. Often, the best outcomes are attained when human expertise and AI strengths are combined. A purpose-built LLM can churn through data and highlight patterns, but it’s the human acumen, category experience and insight-laddering that will ultimately frame those findings in the bigger picture; and come up with creative recommendations. Likewise, a generic AI can produce an answer, but the researcher must judge its validity and relevance.

Generic LLMs are amazing multi-tools – use them for broad tasks, quick drafts and exploration. Purpose-built LLMs are precision instruments – use them when you need accurate, context-aware analysis and you want to streamline your research workflow.

By understanding the differences and considering your needs on aspects like data security, nuance, and actionability, you can make an informed choice. The landscape of AI for insights is evolving rapidly, and it’s an exciting time – the key is to adopt these technologies thoughtfully. Whether you deploy a GPT-4-based chatbot in your research team or onboard a specialized AI platform (or both), keep your goals in focus: better, faster, and more impactful consumer insights. The right AI approach should serve those goals, and hopefully this overview has brought you closer to figuring out which approach that is for your organization.