Research and analysis

The task

You need current, sourced information synthesized into an answer. Market research, vendor comparisons, fast-moving technical changes, and product research all fit this pattern.

Why the old approach is outdated

The old generic prompt was simple: “Research this topic and summarize it.” That collapses two jobs into one.

Retrieval finds current sources. Synthesis turns those sources into a useful answer. Perplexity is strong when the retrieval itself is the product because its Search API returns real-time ranked web results, and its Agent API returns cited, web-grounded answers (the older Sonar chat-completions surface is being folded into the Agent API and is supported only until September 27, 2026). Modern ChatGPT can also search the web and return sourced answers, so the better choice depends on the task surface, not a fixed winner.

The current approach

For retrieval, write the prompt like a search brief. Ask for source-backed facts and define the freshness window.

Compare current pricing and rate-limit policies for OpenAI, Anthropic, and Google
LLM APIs as of August 2026. Cite sources for each figure and flag any pricing page
that does not publish enough detail to compare fairly.

For synthesis, give the sourced material to a reasoning model and ask the decision question last.

<documents>
<document><source>provider_pricing_notes.md</source>{{pricing_notes}}</document>
<document><source>rate_limit_comparison.md</source>{{rate_limit_notes}}</document>
</documents>

Based only on the material above, which provider has the best cost-to-limit fit for
a workload doing roughly 500k tokens/day with bursty traffic? State the recommendation,
the main caveat, and the missing data that would change the answer.

When the source pack is large, use context window management to keep current evidence separate from old notes and abandoned search paths.

When to use a different model

Use Perplexity when you need ranked search results, citation-heavy retrieval, or source controls. Use ChatGPT with web search when you want one model to search, reason, and produce a polished answer in the same workflow. Use Claude, Gemini, or Grok after retrieval when the job is deeper synthesis over a source pack.

For high-stakes research, ask for source dates and quote-free paraphrases. Citations are starting points. They do not remove the need to open the source when the claim matters.

What to avoid

• Using one “research this” prompt for both search and synthesis. Split the work when the answer depends on current facts.
• Asking a search-tuned model a vague conversational question. Give it names, domains, date windows, and comparison fields.
• Treating a single citation as settled evidence on fast-moving topics.
• Letting the model fill gaps silently. Tell it to mark missing data instead of smoothing over it.