AI Keyword Research: How to Use Free Chatbot Tools

AI keyword research breaks down when tools give you long lists that look helpful but tell you nothing about how people actually think or ask questions. You feel it every time you open a keyword tool and see surface-level variations instead of the language users reach for when they’re confused, comparing options, or trying to solve a real problem. And without that nuance, your content strategy stalls before it even starts.

• You get endless head terms while the long-tail phrases that drive action stay hidden.

• You spend hours filtering data but still miss the questions people ask in their own words.

• You build clusters that look clean on paper but don’t reflect how humans explore a topic.

This piece gives you a clear walkthrough of how to use ChatGPT, Perplexity, Claude, Gemini, and Bing Copilot for keyword research, along with the workflows that make each one valuable. Think of it as a practical playbook you can apply today—and if you want to go beyond keyword research into AI search visibility tracking, we added a small bonus at the end to help you get started.

TL;DR

ChatGPT: Best AI keyword research tool for fast long-tail ideas

Key ChatGPT standout features

• Natural language chat that understands plain questions and topics

• Flexible outputs like lists, tables, outlines, and short briefs

• Strong at long-tail and question-based keyword ideas

• Can group keywords into themes, intents, and simple clusters

• Can scan or summarize content you paste in for keyword insight

ChatGPT feels like a smart SEO helper that responds to clear direction instead of complex filters, and it can turn a simple topic into many keyword ideas that match how people speak. Because the model sees language patterns with wide context, it shifts from head terms to long-tail phrases and intent paths without losing the link to the core topic, which helps shape clearer content plans.

This strength grows when you ask it to turn loose ideas into something usable, since it can group phrases by intent, funnel stage, or theme with little guidance. You can also use it to understand competitor pages by pasting text or summaries, which helps reveal missing topics you should cover.

Despite those advantages, there are watch-outs you need to account for. The tool does not see real search volume or difficulty scores, so every list still needs a check in a data source to confirm demand. It can also create phrases that sound logical but do not show up in real search behavior, which means you should verify key ideas before putting time and budget toward them.

How to use ChatGPT for keyword research

A simple way to start is to give the model a topic, an audience, and the pain behind the search. For example, instead of saying “Give me keywords for email marketing,” try: “Give me long-tail keywords and questions for B2B founders who struggle with low cold email reply rates,” and ask for groups like problems, how-to searches, and comparison terms. This gives you ideas tied to real needs instead of generic phrases.

After you have the list, ask ChatGPT to remove duplicates, group the ideas, and add one short line that explains the likely user need behind each phrase. This produces a clean, structured output you can drop into a sheet or validate in a free keyword tool without extra editing.

How ChatGPT supports your keyword research workflow

Best use cases for ChatGPT in keyword research

• Turning one seed topic into many long-tail keyword ideas

• Cleaning and grouping keyword lists by theme or intent

• Mining question-based searches for FAQ or blog structures

• Producing quick briefs that tie keywords to clear content angles

Perplexity AI: Best AI keyword research tool for web-anchored keyword discovery

Key Perplexity AI standout features

• Live web-enabled search that blends real-time information with AI synthesis

• Answers backed by source citations linked to original web pages

• Conversational output that mirrors how people ask full questions

• Multiple AI models available, with switching options on Pro tiers

• Deep research modes for multi-source reports and richer context

Perplexity works more like an AI answer engine than a standard chatbot, since it searches the live web before building a response. Each answer includes citations that point back to real pages, giving you a clear picture of where ideas come from and letting you judge the quality of the information. With this approach, you get keyword ideas shaped by current language and public conversations instead of patterns learned only from past training data.

Because results come from fresh articles and discussions, the tool becomes helpful when you need keyword ideas tied to trends or real questions people ask right now. You can also see how experts and publishers phrase topics across the web, which helps you spot natural language patterns and theme variations you might not find in a standard keyword tool.

There are still limits to consider. The tool does not replace SEO metrics platforms, since it cannot show search volume, difficulty, or cost data. You need another tool to confirm demand before building content. One more caution is the risk of summary errors, because any tool that blends search and AI may miss nuance from the source. This makes it important to click citations and check key terms before you rely on them.

How to use Perplexity AI for keyword research

A helpful starting point is to craft your prompt like a real user query rather than a keyword list request. For example, instead of asking “marketing automation keywords,” you might try: “What questions do small teams ask when trying to choose a marketing automation tool?” This gives you full phrases and long-tail patterns based on natural language.

After you get the answer, review the cited sources to see how writers and sites describe the topic. Pull recurring terms, phrasing styles, and question formats into a list, then look for repeated ideas or angles across sources. This gives you keywords with real-world grounding instead of guesses.

How Perplexity AI supports your keyword research workflow

Best use cases for Perplexity AI in keyword research

• Extracting natural-language search phrases from live web content

• Finding question-based keywords for FAQs and long-form resources

• Spotting trend-driven angles that shape search demand

• Validating terminology against real expert sources

Claude AI: Best AI keyword research tool for deep reasoning and structured content planning

Key Claude AI standout features

• Strong keyword list generation driven by natural-language understanding

• Semantic clustering and content-tree building for clear topic structure

• Competitor keyword extraction when you provide text or summaries

• Long-context processing for large documents and reports

• Multilingual support for global content workflows
  

Claude stands out through its ability to handle complex reasoning and structure large bodies of information into clean, usable outputs. You can feed it long documents, detailed briefs, or full competitor pages, and it will turn them into keyword lists, topic clusters, and outline structures that feel coherent and purposeful. This makes it useful when you need more than a quick brainstorm and want to build a full content plan with logic and hierarchy.

Another advantage is how well it handles organization tasks. The model is skilled at transforming unstructured ideas into neat content trees, map-style clusters, or strategic topic paths that guide editors and writers. It also adapts well to prompts, so you can shape workflows for intent grouping, comparison angles, or semantic themes without training or complex setup.

Yet a few limits should guide your workflow. The tool does not have built-in SEO metrics, so it cannot show search volume, competition, or trend data. You must pair it with outside sources to confirm the demand behind its suggestions. Its performance also depends on the quality of your prompts and the data you give it, meaning competitor analysis only works if you supply the content you want it to evaluate.

How to use Claude AI for keyword research

A simple way to begin is to provide Claude with a topic and a clear goal, then ask for long-tail keywords, intent paths, and a brief content tree. For example, you might say: “Give me long-tail keywords and a topic cluster for people searching for sustainable packaging ideas,” and then ask Claude to group each term by intent. This gives you a structured list with reasoning baked in.

If you want competitive insights, paste text from a rival page and ask Claude to extract recurring terms, missing angles, and semantic gaps. You can then blend these findings with your own keyword ideas to build outlines or refresh older content.

How Claude AI supports your keyword research workflow

Best use cases for Claude AI in keyword research

• Building content trees and topic maps from scratch

• Extracting keyword themes from long inputs or competitor pages

• Creating semantic clusters and intent-based keyword groups

• Supporting multilingual or international SEO planning

Gemini: Best AI keyword research tool for intent-rich ideas and natural question discovery

Key Gemini standout features

• Keyword suggestion through conversational prompts and topic inputs

• PAA-style question generation for FAQ and outline planning

• Topic expansion that surfaces subtopics and semantic angles

• Strong query-intent understanding shaped by Google’s ecosystem

• Generative support for outlines, clusters, and thematic groupings

Gemini functions as a language-first research assistant that helps you uncover how people think and ask about a topic. When you prompt it with a niche, audience, or search goal, it produces keyword ideas and question lists that reflect common search behaviors. Because it sits inside Google’s broader AI environment, it leans toward phrasing patterns and query structures that feel close to the way users frame real questions online, which gives its suggestions a more natural tone.

Its strength shows most clearly in how it handles topic expansion and semantic ideation. The model is good at turning a broad topic into related subtopics, themes, and outline paths that help you shape content around clear user needs. This makes Gemini useful when you want to map the “edges” of a subject and understand what people might want to learn next, which is helpful for planning clusters or building FAQ sections.

Gemini still has clear boundaries. It does not deliver search volume, difficulty scores, competitive metrics, or any of the structured data that SEO tools provide. It also does not include crawlers, SERP insights, or real-time SEO analytics. Because its keyword suggestions come from language patterns rather than direct search telemetry, you need to confirm ideas with a proper SEO database before building content.

How to use Gemini for keyword research

A simple way to use Gemini is to ask for keyword ideas tied to a specific audience or question pattern. For example, instead of asking “keywords for remote work tools,” you might try: “What questions do small teams ask when choosing remote work tools, and what long-tail search terms reflect those questions?” This produces keyword ideas and PAA-style queries in one step.

From there, you can ask Gemini to expand the topic into outline ideas or semantic groups, which helps you see the broader structure around the keyword set. You can then export the strongest ideas into a sheet and run them through a data tool to check demand and difficulty.

How Gemini supports your keyword research workflow

Best use cases for Gemini in keyword research

• Brainstorming long-tail keyword ideas tied to user intent

• Generating PAA-style questions for blog posts and FAQ blocks

• Expanding topics into semantic clusters and outline paths

• Exploring natural-language phrasing that informs content planning

Bing Copilot: Best AI keyword research tool for real-time, web-grounded brainstorming

Key Bing Copilot standout features

• Keyword idea generation supported by live Bing search context

• Conversational exploration that adapts to follow-up questions

• Web-backed summaries and suggestions tied to current content

• Cited sources for easier verification and deeper research

• Integrated across Bing, Edge, and Microsoft’s ecosystem for quick access
  

Copilot acts like a bridge between a search engine and an AI assistant, since it blends generative text with real-time web information. When you ask for keyword ideas or topic angles, it uses Bing’s current results to shape its suggestions, which means you see phrasing, themes, and emerging topics that reflect what is happening online now. This makes the tool helpful for brainstorming around fast-changing subjects, news-driven queries, or niches where trends shift often.

The conversational flow adds another layer of value, because you can refine ideas in small steps. Instead of restarting with a new search, you can ask follow-up questions to clarify an angle, explore related terms, or dig deeper into specific user questions. Copilot also gives links or citations with many of its responses, so you can visit the source pages to validate ideas or gather additional context.

There are limits to keep in mind, especially for SEO work. Copilot does not provide search volume, difficulty scores, click metrics, or any structured keyword data. It cannot show competition levels or performance trends, so you need a true SEO tool to judge which terms are worth targeting. Its keyword suggestions are generated from web context rather than a dedicated SEO database, which means you must confirm each idea with real metrics before building strategy or content.

How to use Bing Copilot for keyword research

A simple workflow begins with asking Copilot for keyword ideas tied to a specific topic and audience. For example, instead of asking “keywords for smart home devices,” you might try: “What questions are people asking right now about choosing smart home devices, and what related topics appear in current search results?” This prompts Copilot to blend generative ideas with what is trending on the web.

After you receive the suggestions, check the cited links to see how publishers frame the topic. Pull recurring themes or fresh phrases into a list, then validate them with a metrics tool to check volume, difficulty, or competition. Copilot works best as an idea generator and trend spotter rather than a full SEO solution.

How Bing Copilot supports your keyword research workflow

Best use cases for Bing Copilot in keyword research

• Discovering trending topics and fresh keyword angles

• Brainstorming early keyword lists before checking metrics

• Pulling question-based queries tied to live search behavior

• Exploring source-backed ideas for research-heavy content

Analyze: The comprehensive AI visibility tracker that takes you further than traditional keyword research ever could

Analyze can help with prompt research through our prompt suggestion feature, which surfaces the bottom-of-funnel prompts buyers actually use across AI answer engines. That gives you a practical starting point for what to monitor and optimize, without guessing which prompts matter or relying on generic keyword lists.

Analyze is not a keyword research chatbot, and it does not replace tools like ChatGPT, Perplexity, Claude, Gemini, or Bing Copilot for brainstorming. Those tools are useful for generating ideas, expanding topics, and mapping intent, but they do not tell you what happens after your brand shows up in an answer. They can suggest language, yet they cannot prove whether that visibility turns into site visits, conversions, and revenue.

Analyze connects prompt visibility to business outcomes. The platform tracks which answer engines send sessions to your site (Discover), which pages those visitors land on, what actions they take, and how much revenue they influence (Monitor). 

You still get prompt-level performance across ChatGPT, Perplexity, Claude, Copilot, and Gemini, but you also see conversion rates, assisted revenue, and ROI by referrer, so “prompt research” becomes something you can measure and improve rather than a one-time brainstorm.

Analyze then helps you act on what you learn. You can optimize the prompts and pages that matter most (Improve), while monitoring how brand sentiment and positioning shift across the wider market over time (Govern).

Key Analyze features

• See actual AI referral traffic by engine and track trends that reveal where visibility grows and where it stalls.

• See the pages that receive that traffic with the originating model, the landing path, and the conversions those visits drive.

• Track prompt-level visibility and sentiment across major LLMs to understand how models talk about your brand and competitors.

• Audit model citations and sources to identify which domains shape answers and where your own coverage must improve.

• Surface opportunities and competitive gaps that prioritize actions by potential impact, not vanity metrics.

Here are in more details how Analyze works:

See actual traffic from AI engines, not just mentions

Analyze attributes every session from answer engines to its specific source—Perplexity, Claude, ChatGPT, Copilot, or Gemini. You see session volume by engine, trends over six months, and what percentage of your total traffic comes from AI referrers. When ChatGPT sends 248 sessions but Perplexity sends 142, you know exactly where to focus optimization work.

Know which pages convert AI traffic and optimize where revenue moves

Most tools stop at "your brand was mentioned." Analyze shows you the complete journey from AI answer to landing page to conversion, so you optimize pages that drive revenue instead of chasing visibility that goes nowhere.

The platform shows which landing pages receive AI referrals, which engine sent each session, and what conversion events those visits trigger. 

For instance, when your product comparison page gets 50 sessions from Perplexity and converts 12% to trials, while an old blog post gets 40 sessions from ChatGPT with zero conversions, you know exactly what to strengthen and what to deprioritize.

Track the exact prompts buyers use and see where you're winning or losing

Analyze monitors specific prompts across all major LLMs—"best Salesforce alternatives for medium businesses," "top customer service software for mid-sized companies in 2025," "marketing automation tools for e-commerce sites." 

For each prompt, you see your brand's visibility percentage, position relative to competitors, and sentiment score.

You can also see which competitors appear alongside you, how your position changes daily, and whether sentiment is improving or declining.

Don’t know which prompts to track? No worries. Analyze has a prompt suggestion feature that suggests the actual bottom of the funnel prompts you should keep your eyes on.

Audit which sources models trust and build authority where it matters

Analyze reveals exactly which domains and URLs models cite when answering questions in your category. 

You can see, for instance, that Creatio gets mentioned because Salesforce.com's comparison pages rank consistently, or that IssueTrack appears because three specific review sites cite them repeatedly. 

Analyze shows usage count per source, which models reference each domain, and when those citations first appeared.

Citation visibility matters because it shows you where to invest. Instead of generic link building, you target the specific sources that shape AI answers in your category. You strengthen relationships with domains that models already trust, create content that fills gaps in their coverage, and track whether your citation frequency increases after each initiative.

Prioritize opportunities and close competitive gaps

Analyze surfaces opportunities based on omissions, weak coverage, rising prompts, and unfavorable sentiment, then pairs each with recommended actions that reflect likely impact and required effort. 

For instance, you can run a weekly triage that selects a small set of moves—reinforce a page that nearly wins an important prompt, publish a focused explainer to address a negative narrative, or execute a targeted citation plan for a stubborn head term.