AI trading retrieval relevance ranking should prioritize evidence that answers the exact research question, covers its required facts and can be traced to an appropriate original source. Semantic similarity is useful for finding candidates, but it cannot establish that a passage covers the right product, account or effective date. Rank eligible passages for usefulness, group repeated reporting by origin, and check what the selected set actually supports before asking an AI to summarize it.
Three search results can look like three confirmations while repeating one announcement. Meanwhile, a less similar account-tier record may contain the fact needed to answer the question. The ranking problem is therefore about both individual passages and the evidence set they form together.
Define relevance as an answerable question
“Find information about trading fees” leaves too much discretion to a similarity model. A more useful request names the venue, product, instrument, account scope, decision time and requested output. For example: “Which spot maker and taker fee schedule applies to this account for this instrument at the stated time?”
Before retrieval, list the facts that an acceptable answer needs: the applicable policy and effective interval, the relevant tier schedule, and the account's tier or rate entitlement. Add discount eligibility and pair-specific exceptions when the actual policy requires them. A general fee explainer can be topically relevant without supplying any of those facts.
Separate eligibility from preference. A document for another product should fail the product check even if its language is an excellent match. A source that was unavailable at a historical cutoff cannot become eligible because it is authoritative today. Among eligible candidates, prefer direct support, appropriate authority and useful additional coverage. A weighted score should not let an impressive headline compensate for a failed scope requirement.
This is a general research architecture, rather than a claim that KLINEO exposes a particular ranking system. Its output is an evidence selection for review, not an authorization to place an order.
Retrieve broadly, then assess the candidates
A candidate generator and a final ranker solve different problems. The first tries to find potentially useful material efficiently. The second decides which candidates best serve the question. The Sentence Transformers retrieve-and-rerank documentation describes an initial lexical or dense retrieval stage followed by a cross-encoder that considers the query and candidate together. Reranking can improve relevance within the candidate pool; it cannot recover an essential document that never entered that pool.
For trading research, literal identifiers can matter alongside meaning. An exact contract code or policy field may be more discriminating than the word “fees.” A practical candidate search can combine exact terms with semantic retrieval, then inspect whether the result set contains the required evidence roles.
When merging search methods, do not casually add their raw scores. Microsoft documents different score ranges for text, vector and semantic ranking in Azure AI Search. Its reciprocal rank fusion explanation describes combining result positions rather than treating the original scores as interchangeable. That establishes how one search implementation merges rankings, not that a high fused rank proves a financial claim.
Save the candidate list as well as the selected results. If an answer misses the account-tier evidence, a reviewer needs to distinguish a retrieval failure from a selection failure. Increasing the number of final passages only addresses some versions of that problem.
Group copied reporting before counting support
A different URL is not necessarily a different origin. Syndicated articles, translated reposts and summaries can all descend from the same venue notice. Retain their relationship to the original object, not just their publisher names. The data-provenance guide explains the underlying source and transformation records.
Exact content hashes help identify identical captures, but formatting and paraphrasing can defeat exact matching. Use canonical source links, attribution, quoted passages and near-duplicate detection to propose groups, then inspect ambiguous cases. Do not merge two revisions simply because their wording is almost identical: a changed exception can make the later version consequential.
Choose a representative that preserves the relevant content and source locator. Often that is the original notice. A secondary report may still deserve a place if it supplies additional reporting, a documented interpretation or another relevant fact. Preserve that contribution explicitly instead of either counting every copy as confirmation or discarding every secondary source.
Goldstein and Carbonell's original MMR research combines query relevance with a penalty for similarity to already selected material. It provides a useful principle for reducing redundancy. Textual novelty, however, does not establish independent observation: two differently worded stories can share one source, while two useful official documents can share one publisher.
Work through a three-passage fee question
The following is a hypothetical retrieval fixture for a fictional exchange. Every document, score and coverage label is invented. All account material is assumed to be obtained through an authorized read, and all required records are assumed to be available at the review cutoff. No row describes an actual venue fee or a captured response.
Assume no additional discounts or pair-specific exceptions. The question requires three evidence roles: N, the applicable spot notice and effective interval; S, the applicable tier schedule; and A, the account's tier. There is room for three passages.
| Candidate | Illustrative semantic similarity | Actual contribution | Origin group | Selection consideration |
|---|---|---|---|---|
| Headline summary A | 0.94 | Notice role N only | N | Repeats the original notice |
| Syndicated copy B | 0.93 | Notice role N only | N | Same underlying evidence |
| Paraphrased summary C | 0.91 | Notice role N only | N | Same underlying evidence |
| Official futures fee page G | 0.88 | Wrong product | G | Fails eligibility |
| Original spot notice D | 0.84 | Notice role N | N | Preferred representative for N |
| Official tier schedule E | 0.79 | Schedule role S | S | Adds a required fact |
| Account-tier record F | 0.73 | Account role A | A | Adds a required fact |
Selecting the three highest similarity scores gives A, B and C. It covers one of three required roles: 33.3%. All three might pass a broad “mentions the right fee change” relevance label, yielding 3/3 relevant results under that loose definition. That apparent 100% precision would hide the unanswered question.
A selection based on the defined roles chooses D, E and F. It covers three of three roles, or 100%, and retains the direct notice rather than three derivatives. These percentages describe this constructed fixture. They are neither measured model accuracy nor evidence that the resulting fee conclusion is correct. The selected records still need to be read and matched correctly.
The result also illustrates why a domain-diversity quota is a poor substitute for evidence diversity. D, E and F can all come from one venue while serving different necessary roles. For a venue-specific fee rule, adding an unrelated publisher merely to increase the domain count would reduce usefulness.
Make the diversity adjustment inspectable
For a larger pool, an MMR-style selection can rank the next eligible passage by its relevance minus its redundancy with what has already been selected. The relative weight is a chosen tuning parameter. It should be evaluated against the research tasks, rather than presented as a universal setting.
For illustration, after selecting D, give a copy a relevance value of 0.90 and similarity to D of 0.95. Give a complementary schedule relevance of 0.80 and similarity to D of 0.20. With an illustrative relevance weight of 0.70, the copy scores 0.70 × 0.90 − 0.30 × 0.95 = 0.345; the schedule scores 0.70 × 0.80 − 0.30 × 0.20 = 0.500. The schedule wins despite lower individual relevance.
Those assigned relevance values are separate from the table's illustrative semantic similarities. Both components need meaningful scales; subtracting arbitrary scores from unrelated models can produce an uninterpretable ranking. A novelty bonus also needs an eligibility floor, or an irrelevant but unusual passage can win. Keep explicit required-role checks alongside the score, because novelty does not guarantee completeness.
Log why each passage was chosen: supported role, origin, scope, captured version, rank and any exclusion reason. Then inspect the actual submitted input. The context-window evidence guide covers the separate problem of selected evidence disappearing during packing or summarization.
Evaluate the set and the answer separately
Build a small judged set of realistic questions with known supporting passages and deliberate traps: a highly similar wrong-product page, three copies of one notice, a missing account record and a nearly identical revision containing an exception. Record the candidate pool and selection before comparing the generated answers.
Check whether required evidence entered the pool, whether eligible direct support reached the selected set, how many slots repeated an origin without adding information, and whether each answer claim cites a passage that supports it. Review broad relevance labels that give duplicate summaries full credit. Keep latency and cost measurements beside these quality checks so a slower reranker earns its place through observed task performance.
Use AI trading prompt regression tests to preserve the expected outcomes when a prompt or model changes. An absent account record should produce an unresolved account-specific conclusion under the fixture's policy, not a guess based on the public standard tier. Check the downstream numbers with numeric unit validation: retrieving the correct schedule does not prevent a percentage-scale mistake.
Freshness and disagreement require their own checks. The retrieval-cache freshness guide addresses whether stored evidence remains eligible for reuse. The contradictory-source guide addresses incompatible claims. Neither issue is resolved by demoting the inconvenient passage until it disappears.
For the next research question, write the required evidence roles before searching. After selection, mark each role supported, missing or unresolved and identify the exact supporting passage. If the highest-ranked results leave a required role empty, improve the retrieval or narrow the answer before producing the summary.