Hello everyone! Following up on Part 1, today we’re going to dive into the concrete, practical ways you can actually leverage semantic search in your daily life and work.
🔄 Changing Your Search Habits: From Keywords to Context
Let’s look at how we need to reshape our search habits to get the most optimal results out of semantic search.
For a long time, we have been conditioned to type core keywords or isolated fragments into the search bar—phrases like "Romeo and Juliet," "Romeo and Juliet famous quotes," or "Romeo balcony." Traditional search engines would then scan the web to find and match documents, news articles, and blog posts that contained those exact, literal words.
However, if you approach an AI-driven semantic search engine by simply tossing in isolated words or keywords as you did before, you actually miss out on the best possible results. This is because AI unlocks its true potential not from the standalone words themselves, but when it reads the underlying "intent" and "context" flowing between those words.
Therefore, when leveraging semantic search, it is highly recommended to input your intended context in the form of full sentences. It is very similar to how you interact with Large Language Models (LLMs) like ChatGPT, Gemini, or Claude—framing your input as a complete command like "Find X for me" or asking a direct question like "What is the reason behind Y?"
In fact, we are seeing a major shift where users increasingly type their queries in the form of complete questions or imperative sentences rather than fragmented keywords. By doing so, they provide the search engine with more comprehensive signals, allowing the AI to grasp the exact context the user intends.
To sum it up, getting the most out of modern search requires a mental and behavioral shift: moving away from the old "word/keyword dumping" approach and adopting a "conversational sentence" format.
🧩 Finding Fragments in Your Memory: From Scenes to Dialogues with Conversational Prompts
Let's take a look at a concrete example to see how sentence-based conversational search actually works in real life. Assuming a scenario where you only have faint, blurry afterimages of a story left in your head, we tossed the following questions into ChatGPT:
<Three Examples of Conversational Prompts Entered into ChatGPT>
What do you think? In all three examples, the phrase "Romeo and Juliet" wasn't typed even once. We simply strung together full sentences of blurry emotions, atmospheres, and fragmented memories floating around in our heads.
In the old days of word-based search, this would have failed miserably or brought up random, unrelated results because the exact keywords didn't match. But context-aware search pieces those memory fragments together to pinpoint the exact classic masterpiece we were looking for. In fact, ChatGPT successfully ranked Romeo and Juliet as its top recommendation for all three prompts, providing highly accurate results.
It even organized the details beautifully, and the summary of the AI's responses is shown in the table below.
One interesting point is that when ChatGPT answered the second "atmosphere-focused" prompt, it also provided images that helped evoke those cinematic moments. It literally showed us visual representations of the unique moods of the masterpieces that matched our query—such as Romeo and Juliet, Wuthering Heights, and Casablanca.
Based on these results, we reconstructed those very scenes using AI image generation technology. Doesn't it feel like those classic, unforgettable scenes that were once just blurry memories are now coming back to life even more vividly?
<Gemini-generated images: Romeo and Juliet (top), Wuthering Heights (bottom)>
💡 Practical Tips for Crafting Prompts
"Whenever I ask a question, the results always seem to miss the mark."
This is the most common trial and error experienced by those who are new to sentence-based search. It usually happens simply because they are not yet accustomed to using conversational search engines. After experiencing this misalignment a few times, many people conclude, "AI still has a long way to go!" or "AI just isn't for me," and quickly revert to traditional keyword search engines.
So, how can you make an AI pinpoint your exact intent within its vector space and deliver accurate results on the very first try? Here are three practical tips to transform the fragments of memory in your head into the exact results you want.
1) Describe the "context that triggers the emotion" rather than the emotion itself.
Many users tend to fill the search bar with emotional adjectives like "sad" or "heartbreaking." However, AI cannot actually feel human emotions, and it perceives an abstract word like "sadness" as far too broad and vague.
Therefore, to guide the AI to target the exact piece of content or scene you are looking for, you must include at least one narrative background or cause that triggers that emotion.
-A Suboptimal Example: A tragic movie about a character who loses a loved one and contemplates making an extreme choice.
-A Better Example: A tragic movie about a character who, despairing under the false impression that their lover has died, decides to follow them in death.
The difference between the two lies in whether you only described the form of the emotion, or whether you clearly specified the concrete event (misunderstanding and despair) that created it. AI understands a user's intent and emotion much more accurately when a clear event or storyline is presented.
2) Provide visual afterimages (spaces, props, etc.) as clues.
Human memories are usually stored as images. Even if you cannot recall the title, visual afterimages like, "What was it? That scene where the two are holding hands on a balcony..." tend to remain vivid. Semantic search engines excel at locating these spatial backgrounds and props.
-A Suboptimal Example: A classic scene where the male and female leads passionately whisper their love to each other.
-A Better Example: A scene where lovers secretly whisper their love on a terrace or balcony in the dark of night.
Try incorporating the spaces, props, seasons, or time of day that remain as afterimages in your head into the prompt. This serves as a crucial clue for the AI to narrow down the exact scene with that specific mood from countless video clips.
3) Specify a concrete "output format."
Even if the AI successfully locates the desired information based on a well-crafted context, you may still waste time sorting through the results if the response is too long or cluttered with unnecessary commentary. It is essential to develop the habit of clearly defining the exact format you want (such as a content list, a specific line, or a particular scene) at the very end of your sentence.
-When looking for the content itself: Recommend a list of 5 classic masterpiece movies that satisfy [condition].
-When looking for the exact dialogue: Provide the famous quotes left by the main character in the story of [content], including both the original text and its Spanish translation.
-When looking for a scene: Show me the iconic scene of [content] in an image format.
Simply adding the habit of specifying the "output format" at the end of your prompt eliminates the hassle of having to repeat follow-up questions to get your desired results.
The event or background that triggered our emotions, the visual afterimages left in our heads, and the desired output format. By effectively combining these three elements, you can utilize LLMs and semantic search in a far more powerful and practical way.
🌐 Notable Semantic Search Platforms
Which content-specialized search engines can take these conversational prompts and locate the exact content, scene, or dialogue you are looking for?
While a wide variety of AI-powered search services are flooding the market today, semantic search specialized for specific media content is still in its infancy. Currently, these technologies are primarily offered as enterprise solutions or web-based demos. Here are three notable platforms that deserve your attention.
1) Video Asset-Focused Contextual Search: Frame.io
Run by Adobe, Frame.io is an enterprise platform specialized in media asset management. Frame.io recently introduced a semantic search feature driven by AI media intelligence, which multi-dimensionally analyzes video visuals (screens), audio (sound), and dialogues (text). It has garnered significant attention from video professionals, particularly due to its robust integration with the Adobe product ecosystem, such as Premiere Pro. However, because it operates as a premium B2B-centered service, accessing these actual features requires creating a specialized account or submitting an enterprise inquiry.
2) Visual Search that Reads the Mood Beyond the Image: Pinterest
Pinterest, a familiar image-sharing platform, supports highly sophisticated contextual search. It is a platform that effectively implements advanced visual semantic search technology. For instance, if you enter sentence-based search queries into the search bar—such as "lovers whispering their love on a balcony or terrace" or "a rainy cyberpunk-style alleyway"—it accurately locates images whose inherent mood and context perfectly match your intent.
3) AI-Powered Web Knowledge Search Engine: Exa
Exa is a next-generation semantic search engine specialized in uncovering the context of web data and knowledge, positioning itself as a compelling alternative to traditional keyword portals. In the past, we entered keyword search queries in a "Google-esque" manner—using phrases like "global OTT platform localization strategy case studies" or "tragic romance novel recommendations similar to Romeo and Juliet." With Exa, however, you can perform much more precise searches through highly contextual queries, such as: "research case studies on AI technology adopted by global OTT platforms like Netflix or Disney+ to enhance subtitle and dubbing quality when expanding into the Asian market," or "a list of classic romance novels featuring a narrative that spirals into catastrophe due to an inescapable fate." While traditional portals yield irrelevant results as the search query grows longer, semantic search engines like Exa actually deliver answers that better align with the user's true intent when the sentences are more detailed and specific.
🎬 Expanding Media Possibilities with Conversational Search
Thus far, we have explored the core principles, real-world use cases, and leading platforms of semantic search—moving beyond the limitations of word-based search to interact through full sentences and rich context.
If traditional search was a process of forcing fragmented words from your head to fit into a rigid search bar, semantic search is a process where AI truly understands human language and intent to guide you straight to the answer. The next time you face a search bar, try setting aside your old keyword-typing habits for a moment. Instead, try throwing out questions or commands rich with specific context. You will experience a dramatic leap in your day-to-day search efficiency.
This shift in the search paradigm is creating even greater innovation beyond our daily routines, particularly on the front lines of the media and entertainment business where massive amounts of data are managed.
LETR WORKS, a Media Intelligence Platform, also enables enterprises to precisely index and manage their vast content assets—including videos, webtoons, and documents—based entirely on context. Moving forward, LETR WORKS will continue to evolve as a platform that seamlessly pinpoints the exact single scene or dialogue a user desires from among thousands of episodes, empowering entirely new creative endeavors and business opportunities.
Thank you for reading!
