When should you use Trip search, and when should you use AskTrip?
Trip offers two ways to explore clinical evidence: search for relevant documents, or ask a question and receive a referenced answer. Which should you choose?
A practical example helps illustrate what each offers:
Is oral or IV iron the best practice for treating iron deficiency in individuals over 65 years old?
This opens up the wider topic. The results include research on iron deficiency in geriatric patients, studies of investigation and outcomes, and the British Society of Gastroenterology guideline on managing iron deficiency anaemia in adults.
That breadth is useful when you want to understand what evidence is available, find a particular guideline, or choose which studies to read. You can filter by evidence type, such as systematic reviews or guidelines, and use the year range to focus your search.
It is also a starting point rather than a finished search strategy. The query does not specify oral or intravenous treatment, so not every result will address that comparison. You can refine the terms as you discover which aspects of the topic matter to your question.
AskTrip starts with the question you want answered. Rather than translating it into search terms, you can ask it in full.
In this AskTrip example, the answer presents oral iron as the usual starting option and identifies circumstances in which IV iron may be preferred, including intolerance, inadequate response or a need for rapid correction.
The answer then expands on the evidence, safety, monitoring and research gaps, with references to explore. It also highlights an important limitation: much of the available research comes from specific settings, such as surgery, and may not directly answer questions about older adults living in the community.
This makes AskTrip useful when you have a focused question and want an initial synthesis to orient your reading. The references allow you to examine the sources behind it; the summary still needs to be assessed in the context of the patient and the applicability of the evidence.
You can then take the question further. The “Explore further” feature lets you ask for more detail or challenge the answer. Suggested follow-ups in this example include when to switch from oral to IV iron and what is known about longer-term health outcomes.
You can also move to Trip search to explore a guideline in full, look for additional studies or widen your reading beyond the treatment comparison.
Equally, you might begin with search, identify an issue that needs clarification, and take that question to AskTrip.
Use Trip search when your priority is finding and selecting the documents you want to examine. Use AskTrip when your priority is a referenced synthesis of a specific question, with the option to explore it further.
Try the two links above with the same clinical problem and see how each supports a different part of finding and understanding the evidence.
We recently received some thoughtful, and slightly concerning, feedback about AskTrip’s answer-confidence display.
A clinician asked about radiation therapy for osteoarthritis. AskTrip’s Clinical Bottom Line was cautious:
Limited and indirect evidence exists regarding the use of radiation therapy for osteoarthritis, particularly in the shoulder, suggesting it remains investigational rather than mainstream treatment.
Alongside this, however, the answer-confidence graphic displayed a green box labelled “Good.”
The clinician and a medical student initially interpreted the green as indicating that AskTrip was more supportive of radiation therapy than another evidence tool they were comparing it with. It took them a little while to realise that the green referred to confidence in AskTrip’s cautious answer, not support for the intervention itself.
This raises an interesting design problem. Most of us have learned to interpret green as “go,” “positive” or “recommended.” Even when the accompanying labels say something different, that immediate visual impression may dominate, particularly when someone is scanning an answer quickly.
We are therefore wondering whether our confidence graphic is communicating what we intended. Would a neutral colour work better? Should “Answer Confidence” become “Confidence in this evidence summary”? Or should the evidence base and answer quality be presented separately without an overall green rating?
We would be interested to hear from other AskTrip users: does the current display make sense to you, or could you also interpret green as endorsing the treatment?
Artificial intelligence offers many ways to improve access to research evidence. The challenge is not generating ideas, it is deciding which problems are most worth solving.
At Trip, we are considering two quite different developments. One would use AI to help people build better searches and identify the most relevant results. The other would monitor guidelines, reviews and clinical topics for important new evidence.
Both could be useful. We are unlikely to develop both at the same time, so we would like your help deciding where to focus.
Option one: AI-supported search
Traditional search remains central to Trip. It keeps the underlying evidence visible and allows users to explore the results for themselves.
But searching can be difficult. People may not know the best terminology, useful synonyms can be missed, and the most relevant result does not always appear near the top.
AI could help by:
suggesting alternative or missing search terms;
turning a natural-language question into a stronger search;
identifying synonyms and related clinical concepts;
reranking results by likely relevance;
suggesting ways to broaden or narrow a search.
The aim would not be to replace search with a generated answer. It would be to make finding the evidence quicker and easier.
This may be most useful if you struggle with choosing search terms, refining queries or finding the best evidence among noisy results.
Option two: Evidence Watch
Evidence does not stand still.
A guideline or systematic review may be reliable when published, but new trials appear, safety concerns emerge, and papers may later be corrected or retracted.
Evidence Watch would allow users to monitor an important topic, guideline, review or recommendation for developments such as:
new trials or systematic reviews;
findings that challenge an existing conclusion;
important safety evidence;
corrections or retractions.
It would not decide that guidance should change. It would act as a safety net, flagging new evidence that may deserve human review.
This may be most useful if you need to keep searches up to date, monitor existing guidance or avoid repeatedly running the same searches.
Which problem causes you more difficulty?
We are not asking which idea sounds more exciting. We want to know which problem has the greater impact on your work.
Would you benefit more from help finding the right evidence today or from knowing when the evidence changes tomorrow?
Please vote in the poll below:
Please also leave a comment telling us what you would need the chosen service to do well.
Your feedback will help us decide what Trip builds next.
We’ve always tried to keep Trip as open and easy to use as possible. But Trip’s content collection and search results reflect years of indexing, classification and curation, and they are increasingly being targeted by large-scale automated extraction, bots and scrapers systematically working through our results to collect records and source links in bulk, rather than people using Trip to answer genuine clinical or research questions.
To protect that work, and to keep Trip fast and reliable for everyone else, we’ll shortly be introducing one change: opening a source document will require either signing in or being recognised through an institutional IP address. Creating a free account takes less than a minute.
Requiring authentication at this final step will also help us identify unusual patterns of activity and investigate potentially abusive use of Trip.
Searching will remain fully open, with no sign-in required, and results will continue to show the title, year and publisher so that relevance and provenance can be judged at a glance. Authentication will only be needed at the final step, when opening the source document.
We know that any additional step should be introduced reluctantly and only where necessary. We believe this is a proportionate way to protect Trip’s infrastructure, the curation behind the service, and the experience of the clinicians and researchers who rely on it every day.
At Trip, we’ve spent thirty years watching people search for clinical evidence. AskTrip lets us watch something we could never really see before: what people ask next.
When someone reads an answer and then comes back with a second question – a refinement – they are telling us how their thinking has moved. This has been made much easier, for the user, via our Explore further feature which was released during our recent major upgrade:
We pulled together a set of these refinement pairs and looked not mainly at their topics, but at their direction of travel. In less than a week, users generated more than 50 such refinements – enough to start seeing patterns in how questions evolve. Two patterns dominate.
Pattern one: from knowledge to action
The most common move is from understanding something to doing something about it. The opening question asks what a thing is, what causes it, or whether it works. The follow-up asks what to do, how to monitor it, or what to choose.
A nurse asks about the perceptions and contributing factors behind medication administration errors, then asks which training programmes actually reduce them. Someone asks what Bartter’s and Gitelman’s syndromes are, then asks about long-term management. A question about whether cladribine retreatment is safe in multiple sclerosis is followed by one about the monitoring protocols to use during it. A question about the weekend effect on organ procurement becomes a question about interventions that reduce weekend discard rates.
The follow-up rarely becomes more theoretical. Once the descriptive need is met, the pull is usually towards clinical application, the last mile of turning evidence into a decision. This maps neatly onto a common gap in evidence tools: they often answer the descriptive question better than the applied one.
Pattern two: from general to specific
The second common move is to sharpen a broad question by adding a constraint – a population, a comorbidity, a comparator, a subtype, or a more specific outcome.
“What is the best treatment for hypertension?” becomes “What is the best treatment for hypertension in patients with chronic kidney disease stage 4?” A broad question about dietary changes for weight loss narrows to whole-food plant-based versus omnivore diets, or to the effect of meal timing. A question about formula changes and infant growth narrows to prebiotics in sick preterm infants. A question about chair alarms for falls narrows to self-releasing chair-alarm belts specifically.
This is essentially the user reshaping a broad query into something closer to a well-formed PICO after seeing the first answer. Occasionally the narrowing is methodological rather than clinical: one user narrowed by evidence tier, asking which quality-of-life measures are highlighted specifically in systematic reviews.
Answer-induced follow-ups
Some refinements look different. Rather than simply narrowing a question the user already had, they pursue a concept that the first answer would plausibly have surfaced. We cannot prove that the concept was not already in the user’s mind without the answer text and a behaviour trace, but the pattern is suggestive.
A comparison of ivermectin versus permethrin for head lice is followed by a question about the prevalence of permethrin resistance – likely one reason the comparison matters. A question about folic acid flour fortification is followed by one about B12-deficiency risk groups, the classic masking harm. A question about whether LLM chatbots in the electronic medical record help clinicians pivots to how clinicians can improve their trust in the outputs.
These matter because they are the organic version of what a “suggest a follow-up” feature is trying to support. They show which answer-embedded concepts users spontaneously find worth chasing. That gives us a direct empirical rationale for building this support, rather than guessing at it.
Two things worth separating out
A related subset probes the evidence itself rather than the clinical content. Users ask whether there have been trials beyond the recommended stroke treatment windows; whether there is any direct evidence on cognitive recovery in brain injury with pre-existing ADHD; whether vitamin D injections deliver clinically relevant benefits; or, in one case, for supporting quotations on the timing of intravenous iron.
That last example is a request for provenance: a user wanting to see the source text, not just a synthesised answer. It is a small group here, but it speaks directly to the need for a show-your-working transparency layer.
Finally, a handful of questions are not clinical in the usual sense. They are about research methodology: predictors of PRISMA 2020 reporting completeness and the effect of AMSTAR 2; quality-of-life measures across reviews; how to structure a conference case presentation. These seem to come from an evidence-synthesis audience rather than from the clinician-facing applied questions. Their refinements deepen the methods question rather than moving towards clinical application. They should probably be analysed as a separate segment; mixing them with applied clinical queries risks blurring both signals.
Why this matters
The refinement pairs may be more informative than the initial questions alone because they capture the natural history of an information need. Initial questions tend to start broad, what something is, whether it works, what the evidence says. Refinements then move in two dominant directions: from knowledge to action, and from general to specific. A smaller but important subset appears answer-induced, where the first answer surfaces a concept the user then pursues.
That has a practical consequence. A follow-up is not necessarily a sign that the first answer failed. Often the answer has done its job: it has acted as a scaffold that helps the user find the sharper question they could not quite articulate at the start.
Refinements are therefore a useful signal not just for what users ask, but for how their uncertainty evolves after they receive an evidence summary. That is the behaviour we want to design around.
One of the most common criticisms of AI in clinical search is the black box problem.
You ask a question, you get an answer, and it is not always clear how the system got there.
For clinical evidence, that matters.
AskTrip takes a different approach. Alongside each answer, users can now view a transparency page, a step-by-step account of what happened between the original question and the response they received.
It is designed to show the working behind an AskTrip answer, not just the final response.
Why this matters
AskTrip is not a general chatbot. It is an evidence-based clinical Q&A system built around Trip Database.
Its job is not simply to produce fluent medical text. Its job is to help users move from a clinical question to relevant, source-linked evidence – while preserving the intent of the question and being clear about the strength and limits of the evidence found.
That means the route to the answer matters.
A confident answer can still be weak if the question was misunderstood. A citation-rich answer can still be misleading if the cited sources do not directly support the conclusion. And an answer can sound clinically useful while quietly drifting away from what the user actually asked.
The transparency page is our attempt to make those risks visible.
The transparency page shows how AskTrip moves from that question to the final answer. It can be reached via the ‘Transparency’ button on each answer page:
1. The question is interpreted
First, AskTrip shows how it understood the question.
In this example, the system identifies the clinical intent as Outlook & Future Care. It also identifies the key elements of the question:
population: patients with benign prostatic hyperplasia
condition: benign prostatic hyperplasia
intervention: transurethral water-jet ablation, also known as Aquablation
phase: treatment
outcome: evidence
Some of these are marked as explicit, because they come directly from the user’s question. Others are marked as implied, because they are necessary to make sense of the question.
This step is important because even a short clinical question carries assumptions. Here, the user is not asking about “surgical removal of the prostate” in general. They are asking about the evidence for a specific minimally invasive treatment for benign prostatic hyperplasia.
By showing the interpreted question, AskTrip makes it easier to check whether the system has preserved the user’s clinical intent.
2. Searches are constructed
AskTrip does not rely on a single search.
For the Aquablation question, the transparency page shows several different search routes:
broad lexical searches
focused lexical searches
PubMed-style searches
vector search using the original question
similar previous questions
The broad searches include natural-language variants such as:
The focused searches are narrower, using terms such as Aquablation, water-jet ablation, benign prostatic hyperplasia, symptom improvement, quality of life, efficacy, effectiveness and recurrence.
The PubMed searches use MeSH and title/abstract terms, while the vector search sends the original question as-is to the semantic search system.
This matters because different search methods find different things. A lexical search may find exact terminology. A vector search may find conceptually similar material. PubMed-style searching may retrieve biomedical literature indexed in a more formal way.
The point is not to trust one search route. It is to gather candidate evidence from several routes, then filter and prioritise it.
3. Results are retrieved
In this example, AskTrip retrieved 292 total results:
107 from vector search
77 from broad lexical search
75 from focused lexical search
33 from similar questions (See ‘reference stripping’ in this blog post)
PubMed found a further 53 results, which were held in reserve and used only if more evidence was needed.
This makes the retrieval stage visible. The final answer is not based on a single hidden query. It is built from a wider set of candidate evidence gathered through several search methods.
4. Duplicates are removed
The same guideline, review or study may be found through more than one route.
AskTrip therefore removes duplicates before moving further through the pipeline. In this example, 292 retrieved results became 202 deduplicated articles.
This matters because duplication can distort the apparent volume of evidence. A document found by three routes is not three separate pieces of evidence. Deduplication helps keep the evidence base cleaner before relevance scoring begins.
5. Relevance is scored
The deduplicated documents are then scored for relevance.
In the Aquablation example, the transparency page shows the score breakdown:
score 10: 11 documents
score 9: 22 documents
score 8: 13 documents
score 7: 12 documents
score 6: 7 documents
score 5 and below: 137 documents
The standard inclusion threshold is 6, but guidelines with a relevance score of 5 can also be included because guidelines may still be clinically important even when their wording does not closely match the user’s question.
This step helps separate documents that merely mention BPH or surgical treatment from documents that are likely to answer the specific question about Aquablation.
6. Documents are prioritised
From the scored results, AskTrip prioritised 26 documents.
These included:
6 essential sources, such as guidelines and systematic reviews
14 desirable sources, such as RCTs, cohort studies and high-quality primary research
6 other sources, such as case reports, opinion or background material
The transparency page also shows where the prioritised documents came from:
23 from vector search
21 from focused lexical search
18 from broad lexical search
5 from similar questions
Those numbers overlap because a single document may be found by more than one route.
This stage is important because AskTrip is not simply counting results. It is trying to identify the documents most likely to support a useful, evidence-aware answer.
7. Evidence is extracted
The next stage is evidence extraction.
In this example, 10 documents were extracted and none were skipped.
The transparency page shows the documents that contributed evidence, including guidelines and systematic reviews. It also labels sources by type, quality and directness.
For this question, several extracted sources were marked as direct evidence. These included material on Aquablation for lower urinary tract symptoms caused by BPH, comparative outcomes against TURP, symptom improvement, safety, sexual function, ejaculatory preservation and reintervention rates.
One source, the AUA guideline amendment, was marked as indirect. That is useful because it shows that not all included sources are treated as equally direct. A document may be relevant and still not provide direct outcome data for the intervention question.
This is a key part of the transparency work. AskTrip is not just listing citations. It is trying to show what evidence was extracted, how it relates to the question, and whether it is direct or indirect.
8. Evidence quality is scored
AskTrip then provides an evidence confidence judgement.
For the Aquablation example, the answer confidence was High. The transparency page explained this using several dimensions:
source quality: direct, high-quality evidence
evidence base: 3/3
answer quality: 3/3
actionability: strong
directness: direct
consistency: consistent
effect signal: clear
sufficiency: adequate
The explanation notes that the evidence for Aquablation in BPH is direct and consistent for outcomes such as symptom reduction, safety and preservation of sexual function. It also notes that multiple systematic reviews and guidelines report improvements in lower urinary tract symptoms and quality of life, broadly comparable to established treatments such as TURP, with potential advantages around sexual function preservation.
These judgements are not meant to replace formal critical appraisal. They are practical signals to help users understand how much weight the answer deserves, and why.
9. The answer is generated and cited
Finally, the answer is generated from the evidence that has passed through the previous stages.
In this example, the final answer cited 8 documents, with a median publication year of 2025.
The cited documents included:
NICE interventional procedures evidence review material
systematic reviews on minimally invasive treatments for BPH
Canadian guidance on male lower urinary tract symptoms and BPH
systematic reviews on ejaculatory function and sexual outcomes
systematic reviews on reintervention rates
French clinical guideline material on surgical and interventional management of bladder outlet obstruction related to BPH
Users can see which documents were cited and click through to inspect them.
The important point is that the final answer is not presented in isolation. It is connected back to the question interpretation, the searches, the retrieved results, the deduplication, the scoring, the prioritisation, the extracted evidence and the evidence quality judgement.
It does not ask for blind trust
The transparency page is not there to make AskTrip look clever. It is there to make AskTrip inspectable.
It helps users see:
whether the question was interpreted correctly
which searches were run
what evidence was retrieved
how duplicates were removed
how relevance was scored
which documents were prioritised
what evidence was extracted
how evidence confidence was judged
which sources were finally cited
This makes it easier to spot problems: a misread question, weak retrieval, over-reliance on indirect evidence, or an answer that sounds stronger than the evidence allows.
Transparency does not make an evidence system perfect. There will still be questions where the literature is incomplete, inconsistent, indirect or poorly applicable to the patient in front of the clinician.
But transparency changes the relationship between the user and the system.
Instead of asking clinicians, librarians and evidence specialists to trust a black box, AskTrip gives them a process they can inspect, question and challenge.
AskTrip turns one today. And we’ve released a huge new upgrade to the site
In its first year, AskTrip has answered more than 20,000 clinical questions – which tells us something important: health professionals want fast, transparent, evidence-based help with the questions they actually face.
To mark the anniversary, AskTrip Q&As are unrestricted for free users until the end of July. It is a good moment to try the new version.
Because a lot has changed.
Tackling EBM wallpaper and intent drift
Two problems kept appearing in user feedback of AskTrip answers.
The first we came to call EBM wallpaper: answers that contained familiar evidence-based language, but did not always get sharply enough to the heart of the question. Evidence-shaped, but not always as useful as it needed to be.
The second was intent drift: where the answer subtly moved away from what the user had actually asked – particularly when questions were complex, ambiguous or clinically messy.
We have substantially reworked the prompts and answer workflow to address both. The goal is to better understand the clinical intent, stay closer to the question, and produce answers that are more directly useful.
From one search to three
AskTrip is only as good as the evidence it finds.
Originally, it used a single lexical search. It now uses three complementary approaches:
two lexical searches, operating on different assumptions and strategies
a vector search, to surface conceptually relevant material that may not use the same wording as the question
This is particularly important for real-world clinical questions, which are often expressed in varied, imperfect or informal language.
Answer score
Is now ‘Answer Confidence’ The old answer score was one dimensional, how good was the evidence used. It’s fine but you could have an answer that used systematic reviews and guidelines and yet was still a poor answer. So, Answer Confidence reflects both the strength of the evidence and also how well the question was answered. It’s a big – positive – change, and the new graphic looks lovely!
More room for nuance
Around a third of users told us answers were too short.
We listened.
Answers are now longer and more detailed, not for length’s sake, but to give proper space to caveats, uncertainty, harms, benefits and practical implications. Clinical questions often deserve more than a paragraph.
Explore further
Clinical questions rarely stop at the first answer.
Explore further lets you interact with an answer directly, asking for clarification, more detail, a follow-up question, or a different angle.
It turns AskTrip from a one-shot Q&A into something closer to an evidence conversation.
Transparency
How we generate our answers should not be hidden. Openness helps users understand the process and that, in turn, improves trust
Export to PDF
Answers can now be exported as PDFs, making it straightforward to save, share or discuss them with colleagues.
New design/page layout
All of this is supported by an updated interface designed to accommodate longer answers, follow-up exploration and the wider feature set.
We are hugely grateful to the subscribers who have supported AskTrip in its first year. Their support has helped us improve the system, expand its features, and build a stronger evidence service. We are also indebted to the numerous testers of the new version of AskTrip – you’ve been brilliant.
Trip connects clinicians to a vast range of articles, guidelines and evidence reviews spread across thousands of external websites. That breadth is one of Trip’s great strengths, but it comes with a long-standing vulnerability.
Websites are redesigned, publishers migrate platforms and documents move. Links break. And when a link breaks, a clinician following the evidence hits a dead end.
This is a problem we have wanted to solve properly for a long time. We are now close to doing so.
Users can already report broken links using the option beneath each Trip search result, and many do. But this only catches the links that someone happens to encounter and takes the time to flag. We have never known what proportion of the broken links encountered by users are actually reported or the true scale of link rot across a database of Trip’s size.
The new system gives us a much more systematic approach.
It checks links automatically and at scale, identifying 404 errors, server failures, timeouts and unhelpful redirects. Because some failures are temporary, flagged links are checked again before being treated as genuinely broken.
Where a link remains unavailable, the system uses the article’s title, date, publication and other metadata to search for a replacement. This is where large language models have made a previously impractical task achievable at scale. Using the article’s title, date, publication and other metadata, the LLM first searches for and identifies a likely new location for the article, something it does remarkably well. It then assesses whether the proposed page is genuinely the same article, rather than relying on title similarity alone. Only when this initial recovery process fails do we move to broader searches through Google, Google Scholar and other sources, with the resulting candidates subjected to a further LLM-based validation check.
Potential replacements found through Google, Google Scholar and other search routes receive an additional LLM-based validation check. The proposed URL must also pass the link checker itself.
High-confidence matches can then be updated and reindexed automatically. Uncertain cases are placed in a review queue rather than being changed on the basis of weak evidence.
We are currently completing the final testing. Once the system is ready, we will run a substantial one-off check across Trip’s eligible records, something we have never previously been able to do. This will be followed by regular checks, so that broken links can be identified and repaired rather than silently accumulating.
For a resource that exists to connect people with the best available evidence, ensuring those connections actually work is exactly the kind of unglamorous – but vital – infrastructure that matters. We are pleased to be getting it right.
At 01:38 this morning, AskTrip answered its 20,000th question. Less than a year after launch in June 2025, that’s a milestone worth pausing on – and the timestamp itself is a useful reminder of something easy to forget when you’re looking at a UK clock. AskTrip’s users aren’t in one time zone, or even a handful: 01:38 in Bristol could just as easily have been mid-morning in Sydney or early evening in Auckland. The “around the clock” nature of AskTrip isn’t really about clinicians burning the midnight oil, it’s that, for a global tool, there is no midnight. Demand for evidence-based answers is continuous, because somewhere it’s always the working day.
On the surface, that reads like a perfectly reasonable specialist query. Look at it a little more closely, though, and it’s actually a nice example of something we’ve started referring to internally as a cross-cutting question, and it’s a useful one to write about, because milestones are as much an occasion for reflection as celebration.
Two questions wearing one coat
“Differential diagnoses for achondroplasia” is a well-defined clinical question in its own right. It points towards the small group of other skeletal dysplasias – hypochondroplasia, thanatophoric dysplasia, pseudoachondroplasia and the like – that can be mistaken for achondroplasia, particularly in early life before the radiological picture is fully established. There’s a solid, settled evidence base for that.
“Differential diagnosis of chronic constipation in children” is also a well-defined question, but a completely different one — it’s a gastroenterology question, classically framed around distinguishing functional constipation from Hirschsprung’s disease, with its own literature, its own red flags, and its own pathway.
The question as posed asks for both at once, but it’s not really clear which one the asker meant – or whether they meant a third thing entirely: what causes chronic constipation specifically in children who have achondroplasia (where spinal or foramen magnum stenosis affecting bowel innervation might be relevant). Three different clinical questions, three different evidence bases, one sentence.
Why this matters for AskTrip
A system built around finding and synthesising evidence for a focused clinical question runs into trouble with questions like this, because there’s essentially never a guideline or systematic review written for that exact intersection. The honest answer requires either picking the most likely intended framing and being explicit about that choice, or addressing more than one framing clearly enough that the asker can see which bit applies to them. Quietly blending the two – taking a bit of the achondroplasia differential and a bit of the constipation differential and presenting it as one coherent list – is the failure mode to watch for, because it can look authoritative while actually answering a question nobody asked.
This is exactly the kind of pattern that’s feeding into the question-type work behind AskTrip 2 – better recognising, at the routing stage, when a question is genuinely compound, and either decomposing it or being transparent about the interpretation that’s been answered, rather than letting the ambiguity disappear into a fluent-sounding response. We’re always striving to improve AskTrip, and examples like this one are genuinely useful – they help us focus on what matters, and we’ll keep working hard to get our approach to cross-cutting questions right.
The milestone bit
None of which takes away from the headline: 20,000 questions in well under a year, with usage continuing to accelerate, is a strong signal that there’s real demand here. But it’s the awkward, messy, occasionally malformed questions – like number 20,000 – that are the most valuable part of that growing dataset. They’re the ones that show us where the next round of improvement needs to go.
Here’s to the next 20,000 – and to learning as much from the tricky ones as the easy ones.
Trip Database Blog 
Recent Comments