How LLMs Like ChatGPT, Perplexity, and Enki Are Transforming Market Research in 2025

From Chatbots to Market Research Engines

Large language models have moved beyond novelty and hype. Once dismissed as chatbots for content generation, they now function as industrial-grade market research engines. Ajay Agrawal put it succinctly: “AI reduces the cost of prediction.” In research terms, that means faster hypotheses, broader evidence gathering, and tighter synthesis across filings, patents, policy dockets, and pilot announcements. In energy and heavy industry, where signals change weekly, organizations that systematize LLM-driven search and reasoning will see opportunities earlier and de-risk decisions sooner.

For a broader perspective on how market intelligence itself is evolving, see The Future of AI Market Intelligence in 2025: From Static Reports to Real-Time Insights.

Making ChatGPT and Perplexity Work Like Analysts

Outcomes depend less on the brand of model and more on how prompts and workflows are designed. Whether you are using ChatGPT, Perplexity, or another out-of-the-box LLM, the key is to structure the reasoning chain. A model should be guided as if it were an analyst: capable of reasoning step by step, searching broadly, and delivering structured outputs that can be trusted.

Well-structured prompts and workflows instruct the model to think step by step: declare assumptions, plan a search, gather sources, reconcile conflicts, and present results with limitations. As W. Edwards Deming reminded us, “In God we trust. All others must bring data.” Effective prompts make data traceability non-negotiable.

Equally important is modeling the output. Analysts should request structured tables for executives, JSON for automation, or bullet points for board memos. Requiring live URLs with dates ensures transparency and verifiability. Finally, prompts should demand deep search, asking the model not only to browse multiple sources but also to reveal what it looked for and did not find. That distinction turns the LLM from a summarizer into a research instrument.

For a higher-level overview of how AI tools fit into market intelligence strategy, see How AI Is Transforming Market Intelligence in 2025.

Prompting with Chain-of-Thought and Deep Search

Chain-of-thought prompting combined with deep search is what turns ChatGPT or Perplexity into reliable research partners. Analysts who define reasoning steps, enforce sourcing rules, and specify structured outputs transform these tools from generic chatbots into engines of market intelligence.

AI Market Analysis with Chain-of-Thought Prompting

Well-structured prompting workflows can compress a two-week market analysis into hours while improving transparency. As Einstein once remarked, ”f I had an hour to solve a problem, I’d spend 55 minutes thinking about the problem and five minutes thinking about solutions.” The same principle applies here: thoughtful design of the workflows matters far more than a single clever prompt

Persona and rules

“You are a Bloomberg-style clean-tech analyst. Think step by step. First list your assumptions. Then outline a search plan with keywords, languages, and sources. Execute the plan with browsing. For each fact, provide a citation with URL and date.

1. drivers_table in Markdown and
2. evidence_log in JSON with fields {claim_id, quote, source_url, date, reliability, notes}.”

Task A: Identify energy market drivers

“What are the main market drivers for Fuel Cells in 2025 to 2030? Include TAM estimates, CAGR ranges, end-use segments, and top policy incentives. Cite analyst houses and filings, not blogs.”

Task B: Capture commercial signals

“List partnerships, JVs, product launches, and investments in Fuel Cells from the last 12 months. For each signal: {company, counterparty, asset or product, geography, date, value if disclosed, primary source URL}. Close with a one-paragraph interpretation of what new entrants imply about demand and which regions are heating up.”

By forcing the model to show its reasoning plan, search widely, and bind every claim to a source, analysts get a product that is both faster and more trustworthy.

AI Market Segmentation: LLM Prompts for Data-Driven Segment Analysis

Segment-level analysis benefits most from explicit reasoning steps, structured comparison, and attention to willingness-to-pay dynamics.

Persona and rules

“You are an energy systems analyst. Think in numbered steps. State uncertainties. Do deep search across patents, pilots, and earnings call transcripts. For every table cell include a citation.”

Task: Fuel cells in data centers

“Analyze Fuel Cells for data center backup and prime power.

1. Identify emerging companies and reference deployments since 2024.
2. Compare value versus diesel gensets on reliability, response time, emissions, TCO, and permitting.
3. Identify which customer segments show willingness to pay a premium and why.
4. Return two artifacts:a) comparison_table with columns {metric, diesel, fuel cell, delta, source}b) buyer_readiness_matrix with rows {hyperscale, colocation, on-prem enterprise} and columns {pain point, adoption signals, blockers, next proof needed} with citations.”

This structure ensures the model not only delivers facts but also highlights uncertainties and adoption barriers, creating a framework for decision-making rather than just information retrieval.

Safeguards for Reliable AI Market Research

Chain-of-thought prompts amplify the strengths of LLMs but also magnify their weaknesses. On the positive side, they excel at multilingual retrieval and synthesis of unstructured text, which is crucial in supply chains where key signals are first published in German, Japanese, or Korean. Risks include hallucinations, overconfident extrapolation, and stale data.

Safeguards must be explicit: require URLs with dates, ask the model to surface conflicting evidence, and log every claim in a separate evidence file. As John Maynard Keynes once remarked, “When the facts change, I change my mind.” A good structured prompting workflow makes it easy to see when the facts change and to adapt conclusions accordingly.

Enki’s Approach to AI-Powered Market Intelligence

Most analysts are still experimenting with ChatGPT and Perplexity at the individual level, writing one-off prompts, copy-pasting results, and then manually validating sources. This is useful, but it does not scale to enterprise needs where consistency, transparency, and speed are critical.

Enki automates this entire workflow. Instead of requiring each analyst to craft chain-of-thought prompts, Enki builds them in by default:

• Automated deep search across patents, policy filings, earnings calls, and press releases.
• Source logs that link every claim to a source URL and date.
• Structured outputs delivered in tables, JSON, or narrative briefs, so results flow directly into dashboards or strategy decks.
• Continuous monitoring of clean-tech signals like hydrogen offtakes, CCUS incentives, or fuel cell deployments.

Figure 1: AI-Powered Market Intelligence

In short, where ChatGPT and Perplexity require analysts to design and validate their own chains of thought, Enki operationalizes the process at scale. Analysts focus on interpretation and strategy, while the platform guarantees the research pipeline is reliable, repeatable, and fast. For a working example, see AI-Powered Energy Market Analysis: Identify Opportunities and Accelerate Your Strategy.

Use Cases: Competitive Benchmarking, Patents, and Early Signals

The biggest returns appear where baseline research is slow, manual, and spread across scattered sources.

Competitive benchmarking in hours:

Analysts can now generate side-by-side comparisons of leading electrolyzer or fuel cell vendors, harvesting disclosures and structuring them into an evidence-linked table. The process that once took weeks compresses to days.

Patent landscape analysis:

Mapping how PEM or SOEC patents transition into commercial applications requires a step-by-step workflow: cluster related patents, group them by companies and technologies, and connect filings to pilots. With deep search, this becomes a repeatable process rather than a one-off task.

Early signal identification in hydrogen and CCUS:

Emerging projects and pilots can be tracked systematically by forcing the model to surface filings and press releases. By also asking for counter-signals, such as cancellations or delayed FIDs, analysts avoid overly optimistic conclusions.

Table 1: Faster Market Research with LLMs

Research Task	Traditional Cycle	LLM with Chain-of-Thought + Deep Search
Competitive benchmarking	2–3 weeks	1–2 days
Patent landscape mapping	4–6 weeks	3–7 days
Policy and regulatory tracking	1–2 weeks	< 1 day

The gain is not only speed. Structured evidence logs improve trust across teams and make insights reusable for finance, strategy, and operations.

Best Practices for Structured Outputs and Sourcing

Structured workflows turn clever prompts into institutional assets. Analysts should always return a compact executive brief plus machine-readable outputs. Commercial signal tables should follow a consistent schema: company, counterparty, asset, date, geography, value, and source URL. Sources must withstand board scrutiny: Bloomberg, IEA, EIA, company filings, regulator fillings, and peer-reviewed work.

Every research run should close with a one-paragraph note on “what would change this conclusion,” Ensuring the process remains transparent and verifiable.

Conclusion: AI Market Research at the Speed of Relevance

Companies gain an edge when they standardize chain-of-thought prompting, deep search, and structured outputs as their default operating system for market research. LLMs like ChatGPT and Perplexity accelerate insights, but reliability depends on designing workflows and prompts that enforce reasoning, evidence, and structure.

For organizations that prefer automation, Enki builds these behaviors into pipelines that continuously monitor patents, policy, and pilots. The goal is not more reports, but faster, evidence-based decisions delivered at the speed markets now demand.