Need help understanding how to use Deepsearch Ai

General
1

I just started using Deepsearch Ai and I’m confused about how to get accurate, relevant results from my queries. Some searches miss key data or return unrelated info, and I can’t figure out if I’m using the wrong settings, filters, or syntax. Can someone explain best practices or share a simple guide so I can use Deepsearch Ai more effectively for research and data analysis

2

I ran into the same thing when I started with Deepsearch AI. Here is what helped me get cleaner, more on point results.

  1. Be specific with your query
    Avoid short, vague prompts.
    Bad: “climate risk report banking”
    Better: “2022 climate risk stress test results for European banks, focus on physical risk, PDF reports”

Add:
• Time frame
• Region or sector
• Document type, like “PDF, research article, 10-K, earnings call transcript”

  1. Use filters early
    Most people skip the filters, then blame the search.
    Right after you search, tighten:
    • Date range
    • Source type
    • Domain or site if available
    If you search “AI safety regulation” and limit to 2023–2024 and “policy paper / gov sources” you drop a ton of noise.

  2. Use negative terms
    If you see lots of irrelevant stuff, add exclusions.
    Example:
    “LLM security evaluation, exclude marketing, exclude press release, exclude blog”
    Some setups use minus signs. Check if “-keyword” works, like Google.

  3. Use the “ask” or “query” box differently from a chat
    Search is not a casual chat.
    Avoid: “Explain what is going on with…”
    Use: “Key risk factors mentioned in 2023 10-K filings for NVIDIA and AMD”

  4. Try stepwise searching
    Do not try one giant super prompt. Split:
    • Step 1: Search narrow topic, like “2023 US bank stress test methodology PDF”
    • Step 2: Open top 3 docs, skim
    • Step 3: New search with what you learned, like “severely adverse scenario unemployment assumptions 2023 DFAST”

  5. Use quoting for exact phrases
    If your topic has common words, lock phrases in quotes.
    Example:
    • Bad: “fair value adjustment banks”
    • Better: “fair value” adjustment “Available for Sale” banks
    Quoting helps avoid random finance content.

  6. Check how Deepsearch “understands” your query
    Most tools have a preview of interpreted query, filters, or “search plan”.
    If it exists, open it.
    You will often see why it pulls wrong data.
    If it overfocuses on one keyword, rephrase with more context words.

  7. Rerank by what you care about
    If there is an option like “sort by date / relevance / citation count / domain trust”, play with it.
    For research-like work, I usually:
    • Filter by last 3–5 years
    • Sort by relevance
    • Then scan domains and pick academic, gov, or known orgs first

  8. Use examples in your prompt
    If your first search is weak, add a small example.
    “Looking for reports similar to ‘World Bank 2020 climate risk and financial stability’ but for Latin America, 2021–2024, PDFs only.”
    Examples act like a pattern.

  9. Keep a “good query” notebook
    I started saving queries that worked.
    You can copy paste and tweak them later.
    Example template I use a lot:
    “Latest [year range] [document type] on [narrow topic] in [region/sector], focus on [2–3 key aspects], exclude [junk terms].”

  10. When it misses key data
    Usually one of these is wrong:
    • Time range too tight
    • Query too broad
    • Wrong document type
    Try:
    • Widen years
    • Add or remove one key concept
    • Change “all sources” to “academic only” or “news only”

  11. When it gives unrelated info
    Tighten:
    • Add domain / industry words
    • Exclude noisy terms
    Example:
    “‘tokenization of real world assets’ blockchain, focus on finance, reports by BIS, IMF, World Bank, exclude crypto exchanges, exclude trading tips”

Once you find a query that returns exactly what you need, re-use its structure. That helps more than any setting in the UI.

3

What @jeff said is solid, but I’d tweak the approach a bit, because over-optimizing the query itself can sometimes backfire in Deepsearch.

A few things that helped me:

  1. Start slightly broader than you think
    If you cram too many constraints into the first query, Deepsearch can “latch” onto the wrong part and tunnel-vision.
    I usually:
  • First run: topic + 1 or 2 key qualifiers, no crazy filters
  • Second run: refine using what actually shows up (sources, terms, document types you saw work)
  1. Use the UI as a debugging tool, not just a filter panel
    Instead of just tightening filters, look at:
  • What “entities” or “topics” the system highlights
  • The snippets in the top 5 results
    If the same irrelevant concept keeps showing up, put that exact word into an exclusion, or rephrase your query to explicitly tell it what you care about more:
    “focus on X, not Y or Z”.
  1. Let Deepsearch read a doc, then pivot
    Once you find even one half-relevant PDF or article:
  • Open it inside Deepsearch (if it has an “analyze” / “ask this doc” mode)
  • Ask inside that doc: “What terms or phrases are used for [your topic]?”
    Then use those exact phrases in your next search. This often works better than guessing jargon in the first query.
  1. Don’t blindly rely on “relevance” sort
    Here I slightly disagree with @jeff. Relevance in these tools can be weird for niche topics. I often:
  • Sort by date first
  • Skim a page or two of titles/snippets
  • Then switch to relevance once I know the right keywords and authors
  1. Treat it like iterative research, not a one-shot answer machine
    When it misses key data, instead of just widening time or changing one term, ask yourself:
  • “Who would realistically publish this info?”
    Then search that publisher or regulator directly + your topic. Example:
    Instead of “European bank climate risk scenarios,” try “EBA climate stress test methodology” or “ECB climate risk 2022 PDF.”
  1. Watch for version and update issues
    Deepsearch sometimes surfaces older, highly-cited docs above newer ones. To avoid basing conclusions on outdated stuff:
  • After you get a useful doc, run a second search: “[doc title] update 2023” or “[org name] [topic] 2023 2024”
    This pulls the “successor” documents that might not rank by default.
  1. Use “compare” style prompts once you have a few docs
    Instead of a giant initial prompt like “tell me everything about X,” do:
  • Search and pick 3–5 good docs
  • Ask Deepsearch: “Compare these documents on [specific question] and highlight where they disagree.”
    This makes the tool act more like an analyst and less like a sloppy search bar.
  1. Build default mini-templates for your use case
    @jeff’s generic template is fine, but it’s more powerful to have 2–3 that match how you actually work. For example:
  • For company filings:
    “[year range] 10-K or annual report for [company], focus on [risk / product / segment], show sections on [keywords].”
  • For academic stuff:
    “Peer reviewed articles on [topic], [year range], focus on empirical studies, exclude review papers and editorials.”

Copy-paste and adjust instead of “being creative” every time. Deepsearch responds better to consistent structure than to clever wording.

If you’re still getting trash results, post one of your actual queries and what you expected to see. It’s usually 1–2 small tweaks away from behaving.

4

I’ll zoom in on things @jeff and @sognonotturno did not stress: how to debug Deepsearch AI when it feels “off,” and how to know whether the problem is your query, the index, or the settings.

1. Quick diagnostic: is it you or the system?

Run this simple test:

  1. Pick something you know exists:
    Example: a well known PDF like “IPCC AR6 climate change 2021 PDF”.
  2. Search that exact title in Deepsearch AI.
  3. If you:
    • Get it in the top 3: ranking is probably fine, your problem is query design.
    • Only find it far down or not at all: the index / filters are likely the bottleneck.

If the “known doc” test fails, stop over tweaking prompts and first check:

  • Active filters (especially source / domain)
  • Workspace / collection scope
  • Any organization level restrictions

Sometimes the issue is simply that Deepsearch AI is not actually searching where you think it is.

2. Use result patterns as feedback

Instead of focusing purely on “wrong result,” look at what type of wrong:

  • Getting lots of blog spam
    → Your source mix is off. Restrict to academic, gov, or reports instead of trying to fix it with 10 extra keywords.

  • Getting almost right but wrong geography / sector
    → Your conceptual query is fine; you just need strong structural anchors like “site:.gov” type filters, region, or known orgs.

  • Getting random, loosely related noise
    → The semantic match is too loose. Switch to more literal phrases:

    • Add quotes
    • Mention specific frameworks, standards, or acronyms

Treat each “bad” page of results as a log file that tells you what Deepsearch is prioritizing.

3. Stop when you hit diminishing returns on query tweaking

I slightly disagree with both @jeff and @sognonotturno here: endlessly refining the text of the query can become a time sink.

Use a 3-iteration rule:

  1. Initial reasonable query.
  2. One refinement using filters and 1–2 exclusions.
  3. One rewrite with clearer intent and maybe quotes.

If after 3 tries you still get junk, change strategy entirely:

  • Search by publisher first. Example:
    “BIS tokenization real world assets PDF” instead of abstract “tokenization of real world assets finance.”
  • Or search a related, easier concept, find a good doc, then pivot from that doc’s vocabulary like @sognonotturno suggested.

If Deepsearch AI is indexed correctly, this “publisher first, then topic” trick often jumps you straight to the right neighborhood.

4. Don’t ignore the snippet layout

A lot of people just scan titles. Deepsearch’s snippet blocks can tell you:

  • What sections it thinks are relevant
  • Which terms it latched onto

If you see that all snippets are focusing on, say, “risk management framework” when you asked about “risk quantification,” then explicitly:

“focus on quantitative metrics, not governance frameworks”

This is different from just adding more keywords. You are telling it what to deprioritize, which can be more powerful than exclusions alone.

5. Use collections / saved docs as your own “mini index”

Once you start finding decent material:

  1. Save the good docs into a project or collection.
  2. Prefer searching inside that collection for follow up questions.

Pros of this inside-collection workflow with Deepsearch AI:

  • Much higher precision once you have a curated set.
  • Follow up queries behave more like a focused research assistant.
  • Easier to keep track of sources you actually trust.

Cons:

  • Initial bootstrapping still depends on global search quality.
  • If your first batch of docs is biased or incomplete, your collection will inherit that bias.
  • Requires some discipline to maintain.

So use global search to “seed” and then downgrade its role over time.

6. When Deepsearch AI misses obvious key data

Instead of only relaxing dates or adding terms, try these checks:

  1. Format problem:
    Are you accidentally excluding the format where that info lives?
    Example: many technical agencies publish crucial details in Excel annexes or machine readable tables, not just PDFs.

  2. Synonym problem:
    Regulators love different words for the same thing. Let Deepsearch help:

    • Ask in a doc: “What terms does this document use for [your concept]?”
    • Then search those new terms globally.

  3. Granularity problem:
    You might be searching at the wrong level. Example:

    • Too high: “AI governance”
    • Better: “model risk management policy for large language models, internal control guidelines, 2023, bank regulator”

If Deepsearch fails repeatedly on a “should be easy” query, that’s your signal to:

  • Change level (macro vs micro)
  • Change who you expect to publish
  • Change format expectations

7. Pros & cons of Deepsearch AI in this workflow

Pros:

  • Strong at combining semantic understanding with literal filters if you give it a bit of structure.
  • Good for cross document synthesis once you have a curated set.
  • Handles long PDFs and technical content decently, especially when you “ask inside” a doc.

Cons:

  • Easy to over trust “relevance” scoring, which can hide newer or niche materials.
  • Quality depends heavily on you using filters and collections; default wide open search can feel messy.
  • If indexing for your org / workspace is incomplete, no prompt trick will fix that, which is frustrating when you are new.

8. How your approach stacks with @jeff and @sognonotturno

  • @jeff is extremely good on front loading structure: filters, negations, templates. Use his style when you already know the domain.
  • @sognonotturno is right about starting slightly broader and iterating. That matters when your jargon is uncertain.

Where I diverge a bit:

  • I would not spend more than a couple iterations polishing the initial query.
  • I lean heavily on known publishers, collections, and the “known doc” test to check whether Deepsearch AI itself is the limit.

If you want more concrete help, post one real example:

“Here is a query I ran, here is what I expected, here is what I actually got.”

With that, it is usually possible to tell in one look whether you are fighting the query, the filters, or the index.