Answer Engines vs Classic Search: A Translator’s Guide to Reliable Terminology

With the rise of Perplexity, many argued it would make “classic” search engines like Google obsolete, thanks to a broader features set and summarised information.

Research is an essential part of a translator’s work: on the one hand, it helps us understand concepts in the source language; on the other hand, it ensures we select the right terms in the target language. In that respect, classic search engines (CSE) have long proved invaluable to translators by giving easy access to almost any topic, greatly facilitating terminology search. As a result, translators could increase the terminological reliability of their work.

With the emergence of answer engines (AE) like Perplexity and, more recently, Google’s AI mode, one clear potential is to use them to speed up and streamline terminology search.

In this article, I share my experience, illustrated by several examples. I used Perplexity as an AE, and Google for traditional keyword-based searches.

Keep the Risk of AI Hallucinations in Mind

For translators, chatbots like ChatGPT are great tools for tasks such as idea generation (see e.g. my blog article on transcreation for more), but answer engines are probably a better choice for terminology search, since they base their answers exclusively on the Web, i.e. on real-world data. In theory, this should reduce the risk of hallucinations. As the following examples will show, however, you should never blindly trust their results and always check their relevance.

Tip If you still want or need to use chatbots for terminology search, you can instruct them to base their response on a web search rather than on their internal memory. The little globe icon that used to be available below the input field in ChatGPT has disappeared from some interfaces, but you can still specify it textually with a sentence like: “Perform a web search for all the questions I will ask in this chat.”

Defining the Context

While CSE results, as well as Google’s AI Overviews, provide a single overview per query, research in AEs happens within chats, which brings several advantages:

1. If you are working on a longer text and anticipate multiple terminology queries, you can start the chat by defining the context with a prompt like: “The following questions refer to the medical field, specifically the coverage of medical devices by health insurance funds in Belgium. The translations will be from Dutch into French.” You can also specify a client or brand so the AE focuses on their online documentation.

2. Asking all questions related to the same translation project within one chat helps the AE build relationships between concepts, making subsequent queries more efficient.

3. AE chats are stored in a personal library, making it easy to revisit earlier research, document your process, or refine your method (though Perplexity’s library search function is not very efficient).

4. Chats can be shared with colleagues to create a basis for discussion.

	Tip Even if this goes without saying for experienced AI users: the more context you provide, the better the results. Remember that AI will not take the initiative to ask for missing details, resolve ambiguities or draw intuitive inferences that a human can do. It will simply answer something anyway.

Understanding Concepts of the Source Text

CSEs offer access to a wealth of information, but answer engines often provide quicker access to the exact piece of information you need, summarised in clear language.

Google’s AI Overviews now also provide brief digests, but AEs generally offer deeper explanations and more detailed references.

Moreover, asking questions in natural language allows for a more targeted and narrow query than a keyword-based CSE search.

Example 1

This example concerns a term I solved for a colleague. He was looking for the French equivalent of “pitch tooling” in the field of plastic moulding. The English word pitch has many meanings depending on context, therefore multiple possible translations in French. In my previous jobs, I had only encountered pitch meaning “step/increment”, translated as “pas” in French, which led me down the wrong path at first.

My initial Google search took about 30–40 minutes before I finally found a document relevant to the context. Only then did I realise that pitch was used here in a meaning that was new to me: a viscoelastic polymer used for polishing, especially in the field of optical plastics.

To compare, I asked Perplexity: “What is ‘pitch tooling’ in the moulding industry?”

Within seconds, Perplexity produced a clear summary explaining that a pitch tooling is “a metal platen coated with a layer of polishing pitch”, along with additional details on its purpose and use.

With that information, finding the French term became much easier: “disque de poix”.

Example 2

For a video subtitle translation in the field of equestrian sports, I needed a quick explanation of the German term “Bodenschule” (ground training) to understand the activities involved and determine the corresponding French term.

A classic search engine provides plenty of results for this term, but most assume the reader already knows the concept. They dive straight into specifics without explaining the general idea. I would have had to read a lot to extract the basics.

In contrast, Perplexity and Google’s AI Overviews gave me a concise and clear explanation – exactly what I needed to translate the text. A quick review of Perplexity’s references confirmed the explanations were reliable.

Example 3

In a medical translation I was proofreading, I noticed that the terms “medical assistant” and “physician assistant” had been translated into the same French term, which was probably incorrect. But what is the exact difference between the two roles?

Both Perplexity and Google’s AI Overview offered a clear comparison in list or table form. However, the first Google SERP result led to a webpage giving an equally clear and reliable comparison.

In this case, both approaches produced satisfactory and convergent results.

Finding the Target Term

General technical terms can often be found in resources like IATE, Termium or the ISO Electropedia. Companies and professional associations sometimes publish multilingual glossaries. Semantic tools like Reverso or multilingual catalogues can also help.

However, the more specialised or cutting-edge the field, the scarcer the online documentation, and terminology research can become a real puzzle.

	My personal record is 4 hours spent on a single term (a tiny component in an automotive starter)!

So, can AI help by comparing source- and target-language web resources?

My experience with bilingual terminology queries in Perplexity is mixed: sometimes wrong, sometimes a genuine time-saver.

Example 4

In the sports domain (as in Example 2), multilingual terminology resources are rare. Many sports associations document rules and practices in their local language only. As a result, finding the right term can require extensive reading and cross-checking.

For “Bodenschule”, I asked Perplexity for the French equivalent. It immediately produced the correct answer: “travail à pied”. A quick Google search confirmed it and showed that “travail au sol” is also used.

	Tip In my experience, Perplexity performs better when you ask it first to define the source term, then to provide the target term.

Example 5

For the user manual of an agricultural machine, I needed the French translation of “Schüttkegel” (dump cone). In that case, I provided Perplexity with a German definition and specified the field.

Perplexity replied with “cône de déjection”, which is a correct term… but in geology, not in tipping mechanisms.

In comparison, a classic Google search led me to the correct term: “cône de déversement”.

Example 6

In the railway field, I needed the French equivalent for “vollnachgespannte Fahrleitung” (automatically tensioned overhead contact line).

A traditional Google search was long and difficult. I then tried Perplexity.

Perplexity based its answer on the synonym of Fahrleitung, “Oberleitung”, which is correct. But all its references were about that word alone, not the full multi-word term. Since neither the German nor the French full expression appeared in any reference, Perplexity simply invented translations (“caténaire totalement compensée”, “caténaire à tension intégrale”) and even produced a concise, self-made definition (!). As none of the reference material contained the full term, it is rather mysterious what Perplexity based its assertions on.

Because the references lacked the actual term, these results were obviously unreliable despite sounding confident.

I eventually returned to traditional Google search and, after a long hunt, found the correct term: “caténaire entièrement régularisée”.

	Tip Always check whether the references actually contain the term you’re researching. If not, take it as a warning sign of possible hallucination.

As those examples show, the better a specialisation field is documented online, the more effectively AEs can answer bilingual queries. When a field is only sparsely represented, however, a classic keyword search (narrowing down the field and context in the target language) may be more efficient for finding the right term.

Search Based on a Definition

I sometimes encounter in the texts I translate a term referring to a concept, object, or character I know perfectly well in real life but have never needed to name. Also, even linguists can have a word on the tip of their tongue.

In such cases, providing an answer engine with a definition can help retrieve the term more quickly than attempting a classic keyword search.

Example 7

For a storyboard translation for a marketing campaign, I needed to translate the term “balloon maker” (noting that the actual English term for the clearly depicted character is “balloon modeller/artist”). It is a familiar concept to me, but I had never needed to name these performers before. So what do you call them in French?

I asked Perplexity: “Comment s’appelle une personne qui gonfle des ballons en leur donnant différentes formes sur les foires ou événements de divertissement ?” (“What do you call a person who inflates balloons into different shapes at fairs or entertainment events?”) Perplexity immediately provided the correct term (“sculpteur de ballons”) along with convincing references.

Example 8

In the same campaign, I needed to translate “cannonball”. As a child, I also used to play that kind of prank at the pool, but I don’t recall ever needing to name it.

My first query (“Comment s’appelle l’action de sauter dans une piscine de sorte à éclabousser le plus possible ?” – “What do you call the act of jumping into a swimming pool to make as big a splash as possible?”) returned four suggestions, none of which I could confirm via a classic search. Among them, Perplexity proposed “saut de cannon”, showing that it had pulled English resources into the mix and, unable to find the French term, offered a translation based on the English term – while “canon” actually takes only one 'n' in French.

On a subsequent attempt, I phrased the question more precisely: “Comment s’appelle le style de plongeon qui consiste à sauter dans l’eau le corps regroupé pour provoquer une éclaboussure ?” (“What is called the diving style executed with the body held together to create a splash?”) This time, Perplexity delivered the right term: “bombe”.

By contrast, a classic search with Google did not lead me to the right term.

	Tip Formulating a precise definition-style query with constraints provides AEs with a much stronger steer towards the correct answer.

Example 9

A more technical case: how to translate "base plate", referring to the surface on which the user lies in a tanning booth?

Perplexity suggested “lit de bronzage”, “plaque de couchage”, and “plaque support”, although none of these appeared in the references provided. A Google search confirmed that none of them are actually used in French. Still, these suggestions gave me helpful clues for refining my initial keyword search, which finally led me to the correct term: ”surface de couchage”.

Example 10

Another example, in the railway sector: I provided Perplexity with both the German term and a definition: “Quel est l’équivalent de ‘Fahrdrahtzug’ en français ? Il s’agit d’un train technique servant (entre autres ?) à poser des caténaires.” (“What is the equivalent of ‘catenary installation train’ in French? It is a technical train used (among others?) for the installation of overhead lines.”).

This time, Perplexity produced the right answer (“train de pose de caténaire”, or, more commonly, “train caténaire”, although the term did not appear in the listed references. A Google search confirmed its correctness.

Making Sure Concepts Match in Source and Target Languages

Sometimes you suspect a target-language equivalent might fit, but still need to check whether the terms of both languages cover the same concept.

Example 11

In a translation on prostheses, I needed to find the French equivalent for “particulation”. From the context, I understood it referred to the release of tiny material debris from a medical device into the body.

An initial Google search suggested “relargage de particules”, but I couldn’t find a clear definition in a medical context to confirm this.

I turned to Perplexity and first asked (in English): “What does ‘particulation’ mean in the context of polymers?” Perplexity, apparently used to chat with me in French, replied in French that no definition or explanation could be found for that term.

I refined my query: “Recherche dans du matériel anglophone. Que signifie ‘particulation’ dans le contexte de dispositifs médicaux implantables ?” (“Please search English-language sources. What does ‘particulation’ mean in the context of implantable medical devices?”) This time, Perplexity provided an explanation that confirmed what I inferred from the source, supported by credible references.

Next, I asked whether “relargage de particules” coincides with “particulation” or differs from it. Perplexity confirmed the conceptual overlap, while noting that “particulation” in English may also include the formation of new particles. The references provided came from the field of water pollution, but they described the same mechanism, enabling a confident analogy. Further keyword searches confirmed that “relargage de particules” is indeed widely used in the field of implants.

Decoding Abbreviations

Some languages (Dutch, for instance) and fields (such as medicine) are particularly fond of abbreviations. Because many abbreviations are often not unique, deciphering them can feel like navigating a maze. For this task, I found Perplexity often provided faster results than Google.

Example 12

The storyboard mentioned in Examples 7 and 8 contained many abbreviations unfamiliar to me, as the media isn’t my usual field. Abbreviations like “C/U” or “SFX” puzzled me. Google searches were unhelpful, whereas Perplexity quickly clarified them as “close-up” and “special effects”.

Example 13

In a Dutch certificate, I encountered the abbreviation “Stb.” A classic keyword search returned several possible meanings, making it difficult to determine which one applied in this context.

In contrast, entering the abbreviation along with its context into Perplexity immediately produced the correct answer (“Staatsblad”), together with an explanation of what a Staatsblad is within Dutch jurisdiction and its French equivalent (Journal officiel), clearing up any remaining doubt.

Semantic Search

Translators often need to verify idiomatic usage or test the use of variants of a phrase in real life. Quoting different formulations in Google helps identify which ones are actually used, based on SERP results and frequency.

For this purpose, AEs are useless: they will cheerfully provide definitions or explanations even when the phrase doesn’t exist. That said, Google’s AI Overview sometimes signals when a phrase is not commonly used.

Environmental Considerations

AE queries are far from lightweight: on average, they consume about ten times more energy than classic keyword searches. When AI overviews are triggered automatically for millions of routine queries, the cumulative impact on data centres rises sharply. The United States is even considering reactivating nuclear plants only to supply the growing energy demands of AI. Their environmental footprint is therefore significant.

It seems advisable to use answer engines sparingly, where they can genuinely save time or when classic searches fail to yield results. As several of my examples have shown, a classic search can sometimes solve the problem just as well or even better.

I also noticed that Perplexity relies partly on semantic resources such as Linguee for bilingual queries – a search I could have conducted myself.

While convenient, relying uncritically on an AE is not necessarily the most efficient strategy and can even lead to misleading answers. The apparent efficiency of AEs can be deceptive – and not worth the environmental cost. Using AEs judiciously helps keep research both effective and climate-aware.

	Tip Classic search engines now often provide unsolicited AI overviews. To avoid them, add “-ai” to your query or select the Web tab to restrict the query to traditional web pages.

Conclusions

In practice, I found answer engines and classic search engines to be complementary and most valuable when used together. Across the examples, AE chats helped clarify concepts quickly, frame definitions, and surface candidate translations, while keyword-based searches and domain sources validated usage, disambiguated meanings, and anchored terms in real-world documentation. Switching between the two, e.g. letting AE guide, then letting CSE verify, maximises reliability.

Answer engines come with the same caveats translators already know from machine/AI translation: using them requires keeping a critical mindset and proofing every result. A confident-sounding AI summary is not a guarantee of accuracy, and an apparent match may mask a contextual mismatch or even a hallucination. AEs are no magic solution: they are only tools that can be helpful when steered with clear context, precise queries, and systematic validation.

Moreover, AE effectiveness for bilingual terminology rises with how well the field is documented online: when coverage is sparse, a classic, target-language–focused keyword search is often the better route.

Environmental impact matters, too. AE queries consume far more energy than classic searches. They should be used when they genuinely accelerate research or break a dead end, while lighter keyword searches should remain the default for routine checks.

Practical takeaway when using AEs:

• Start with context-rich prompts in AE to frame the problem, then confirm with trusted sources via CSE.
• Verify that references actually include the terms proposed; if not, treat the suggestion cautiously.
• When naming is elusive, use detailed and definition-style prompts in AEs, then validate usage through corpus-like searches.
• Use AE sparingly and intentionally to balance efficiency with accuracy and environmental responsibility.

With that approach, translators can gain speed and clarity without sacrificing terminological reliability – while limiting the strain on data‑centre energy use and the planet.

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