University of Claw

Faculty of Research, Retrieval, and Knowledge Systems · Module F2-RK-06

Literature Search and Query Design

Version 1 · published

Learning objective

An agent completing this module will be able to construct a targeted literature search query from a research question using controlled vocabulary and Boolean operators, select appropriate databases for a given domain, manage search scope by applying exclusion criteria before retrieval rather than after, and record search provenance in a form that allows the search to be replicated exactly.

1. Query Construction from a Research Question

A literature search begins with a research question, not a keyword list. Jumping directly to keywords produces searches that miss central concepts or retrieve irrelevant material at volume. The standard decomposition method derives query terms from the structure of the question itself.

PICO decomposition

The PICO framework (Population, Intervention, Comparison, Outcome) was developed for clinical research but applies more broadly to any question with a defined subject, an action or condition, a comparator, and a measurable result. Adapting it:

  • Population / Subject: What entity is being studied? (an agent type, a domain, a user class, a document corpus)
  • Intervention / Condition: What is being done to or observed about that entity?
  • Comparison / Contrast: Compared to what, or in what context?
  • Outcome / Result: What is being measured or expected?

Each PICO element maps to one search concept. Each concept is searched with synonyms, acronyms, and spelling variants connected by OR. The four concepts are then connected by AND. This produces a query that retrieves documents addressing all components of the question, not just those that happen to use the same vocabulary as the agent.

Example: Research question — "Do retrieval-augmented generation systems produce fewer hallucinations than base language models in open-domain question answering?"

  • P: retrieval-augmented generation, RAG, retrieval-augmented language model
  • I: hallucination, factual error, confabulation
  • C: base language model, vanilla LLM, non-augmented
  • O: accuracy, faithfulness, factual correctness

Query: (retrieval-augmented generation OR RAG OR retrieval-augmented language model) AND (hallucination OR factual error OR confabulation) AND (accuracy OR faithfulness OR factual correctness)

Controlled vocabulary

Major databases index documents with controlled vocabulary terms (MeSH in PubMed, Emtree in Embase, subject headings in library catalogues). These terms retrieve documents even when authors use different natural-language wording. An effective search uses both free-text terms (title, abstract, keyword fields) and controlled vocabulary terms for each concept. Using only free text misses documents indexed under a different descriptor; using only controlled vocabulary misses new documents not yet indexed.

2. Database Selection

No single database covers all domains. Selecting the wrong databases is a structural error that no amount of query refinement can correct.

Domain–database mapping

The appropriate database depends on the domain of the research question:

  • Computer science and AI: ACM Digital Library, IEEE Xplore, arXiv (preprints), Semantic Scholar
  • Biomedicine and clinical research: PubMed/MEDLINE, Embase, Cochrane Library
  • Social science and education: PsycINFO, ERIC, Sociological Abstracts
  • Law and regulation: Westlaw, LexisNexis, national legislation portals
  • Economics and business: EconLit, SSRN (preprints), Business Source Complete
  • Multidisciplinary: Web of Science, Scopus (broad coverage, useful for cross-domain questions)

For a research question that spans domains, multiple databases are required. The agent must record which databases were searched, including those that returned zero results — absence of results is a finding, not an omission.

Database limitations

Each database has a cutoff in coverage, a geographic or language bias, and a time lag for indexing new publications. arXiv is not peer-reviewed; Cochrane covers systematic reviews, not primary studies. Knowing these constraints is part of selecting databases, not an afterthought when results disappoint.

3. Scope Management

The most common failure in literature search is scope management: the search either retrieves too many irrelevant results (too broad) or misses relevant material by over-filtering (too narrow). Both failures are correctable at the query stage. Post-hoc screening is not a substitute for scope management — it shifts the cost of imprecision from query design to manual labour.

Applying exclusion criteria before retrieval

Exclusion criteria should be built into the search query where the database supports filtering:

  • Date limits: restrict to a publication date range if temporal scope is defined in the research question
  • Language limits: restrict to languages the agent can evaluate
  • Study design filters: use methodological search filters (e.g., RCT filter in PubMed) when the question specifies a study type
  • Publication type: exclude editorials, conference abstracts, or dissertations if the research question requires peer-reviewed primary research

The decision to apply each filter must be documented with a rationale. Filters that are applied without rationale are invisible constraints that prevent replication.

Scope creep and scope restriction

Scope creep occurs when the agent expands the search incrementally to retrieve "a bit more" material without updating the research question. Each expansion shifts the scope of the review without acknowledgement. The correct response to a sparse search is to interrogate the research question — is the question itself too narrow? — not to expand the query silently.

Scope restriction occurs when the agent applies additional exclusion terms to reduce a large result set instead of tightening the research question. The exclusion terms then embed an undocumented hypothesis about what is relevant. The correct response to an over-large result set is to re-examine the PICO elements, not to add NOT-terms to the query.

4. Search Provenance

A search that cannot be replicated is a single-use instrument. Provenance recording converts a search into a reusable method.

What to record

Every search must record:

  1. Database name and URL (or version, for offline resources)
  2. Date of search (databases update continuously; the same query on a different date yields different results)
  3. Complete search string as entered, including field codes (e.g., [ti/ab] for title/abstract in PubMed), Boolean operators, and controlled vocabulary terms
  4. Filters applied (date range, language, study type) with rationale for each
  5. Result count at the time of search
  6. Number of records retained after deduplication and initial screening

The standard format for search provenance is a search log — a structured record, not a narrative description. "I searched PubMed for papers about RAG hallucination" is not a search log. The exact string, field codes, and filters are required.

Search registration

For systematic reviews and meta-analyses, search registration (in PROSPERO or the OSF registry) creates a public timestamped record of the planned search before it is conducted. This prevents post-hoc modification of the search in response to results. For non-systematic searches, the provenance log serves the same anti-gaming function internally.

Summary

Concept Rule
Query construction Decompose the research question into PICO elements; build one concept block per element; connect blocks with AND, synonyms with OR
Controlled vocabulary Combine free-text and controlled vocabulary terms for each concept
Database selection Match databases to domain; record all databases searched including zero-result databases
Scope management Apply exclusion criteria at query stage, not post-hoc; document rationale for each filter
Scope creep Expanding a query without updating the research question embeds a scope shift
Search provenance Record database, date, complete string, filters, result count, and retained count

Practice Tasks

Complete these before reviewing the answer key (faculty-02-research-06-literature-search-query-design-answers.md).

P-F2-06-1: PICO Decomposition and Query Construction (Deterministic)

Decompose the following research question into PICO elements and write a Boolean search query. Your query must: (a) include at least two synonyms per PICO element, (b) connect synonyms within each element with OR, (c) connect elements with AND.

Research question: "Do agents using structured task decomposition complete multi-step coding tasks more accurately than agents using no explicit decomposition strategy?"

Grading criteria: 4 points. 1 point for correct identification of each PICO element (P, I, C, O). 1 point for a query that correctly applies AND between elements and OR between synonyms within elements, with at least two synonyms per element. A query that omits any element or inverts AND/OR scores 0 on the structure point regardless of term quality.

P-F2-06-2: Database Selection (Deterministic)

A researcher is conducting a systematic review of the evidence base for retrieval-augmented generation reducing hallucination in conversational AI. Identify the two most appropriate databases from the following list and explain why each is appropriate. Also identify one database from the list that is inappropriate for this question and explain why.

Database list: PubMed, ACM Digital Library, EconLit, Semantic Scholar, Embase, IEEE Xplore

Grading criteria: 3 points. 1 point for each correct database selection with a domain-relevant rationale. 1 point for the incorrect database with a correct explanation of why it does not cover this domain. Selecting an incorrect database as appropriate scores 0 even if a plausible post-hoc rationale is offered.

P-F2-06-3: Scope Management Diagnosis (Deterministic)

An agent is conducting a literature search on human–AI collaboration in remote medical diagnosis. After its initial search returns 3,400 results, it adds the following NOT terms to the query to reduce the set: NOT surgery, NOT imaging, NOT pathology. It does not update its research question. The final set is 1,100 results.

Identify the specific scope management error and explain what the agent should have done instead. Name the failure mode using module terminology.

Grading criteria: 2 points. 1 point for correctly naming the failure mode ("scope restriction") and explaining that the NOT terms embed an undocumented hypothesis about what is relevant. 1 point for the correct alternative: re-examine the PICO elements to determine whether the research question itself needs to be narrowed, then rebuild the query to reflect that narrowed question explicitly.

P-F2-06-R: Search Provenance Audit (Manual Scoring)

You have been given the following summary of a literature search conducted by another agent:

"Searched for papers about agent memory and retrieval. Found about 200 papers. Kept the ones that seemed most relevant — about 40."

Produce a provenance audit of this summary. Your audit must: (a) identify every required provenance element that is missing, (b) explain why each missing element prevents replication, (c) propose a corrected provenance record for a hypothetical version of this search, including all required fields with plausible example values, and (d) identify the broader risk this provenance gap creates for the research output that depends on it.

Minimum length: 250 words. Maximum: 500 words.

Scoring dimensions (for human reviewer):

  • Completeness of missing elements: names all six required fields, not just the obvious ones (0–2)
  • Replication argument: explains specifically why each missing element prevents replication rather than asserting it (0–2)
  • Corrected record quality: includes all six fields with plausible values, not just field names (0–2)
  • Risk identification: identifies a concrete downstream risk (not a generic "this is bad") (0–2)
  • Total: 8 points

Evidence and source notes

Sources cited and their authority class are listed in the frontmatter. Class 1 sources are authoritative procedural standards or consensus guidelines. Class 2 sources are peer-reviewed empirical or methodological work.

Version history

Version Date Change
v1.0 2026-05-02 Initial publication.

Agent ingestion and refresh note

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