Shadowing Practice: AI Agent Memory Architecture: Structured SQL, Semantic Vector Search & Memory Bank - Learn English Speaking with Video
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Hi, I'm Annie.
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If your agent can hear, see, and take action, the next question is, what should I remember after the session ends?
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In this episode, I will show you the architecture for long -term memory in a multi -model agent,
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facts in structure memory, understanding in semantic memory, and raw audio or video outside of memory.
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Hi there.
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Hey there.
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Good for that customer dinner tomorrow
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or just browsing oh i want
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you suggest something for customer dinner tomorrow based on my wish list for sure you have those leather ankle boots
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and a classic black pump on your list either would go great with
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that red cocktail dress you bought but
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if you want something a bit more relaxed for dinner maybe mix it up with the plaid button -up
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and classic denim jeans all right let's start with the two types of the memory when you're talking to a multimodal agent.
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It feels like the agent remembers everything.
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It remembers what you said five minutes ago.
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It can use what it just heard.
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But most of that is short -term memory.
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It is like RAM.
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It is useful while the session is running, but it's gone when the session ends.
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So the real design question is not, can we save everything?
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The real question is, what is worth carrying into the next session?
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And And that memory comes in two kinds.
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The first kind is facts.
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Facts are exact.
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You have a dinner on Thursday.
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You bought the blue jacket.
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Those are things you can put in rows and columns.
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They have IDs.
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They have dates.
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They can be created, updated, and deleted.
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That is structured memory.
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The second kind is understanding.
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Understanding is not one clean role in a table.
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It is an age in learning what something means about you.
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You prefer simple over flashy, you run warm, you reach for dark colors when you want to feel confident.
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That is not just a fact to look up.
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That is a pattern.
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It is a difference between knowing your calendar and knowing you.
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So, here is the first takeaway.
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Facts are exact.
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You look them up.
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Understanding is meaning.
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You recall it.
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Now let's talk about the decision that makes or breaks your memory system.
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Those two kinds of memory should not live in the same place.
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Facts belong in a structured database.
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That usually means SQL, Postgres, MySQL, Cloud SQL, and Google Cloud whatever fits your stack.
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Why SQL?
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Because facts need exact answers.
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If I ask what is on my calendar Thursday, I do not want a similar memory.
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I want an exact event.
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The database should return the actual roles.
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That is what structured databases are good at.
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Now, understanding belongs somewhere else.
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Understanding stays in a vector store.
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A vector store lets you search by meaning.
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So instead of asking, find a role where preference equals dark colors,
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the agent can ask, what do I know about any style preference for this kind of event?
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And the memory system can bring back memories that are semantically related.
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And that is the job of embeddings and vector search.
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Here is an important detail.
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VectorStore is a category.
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It is not one specific product.
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You could use PG Vector inside Postgres.
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You could use AlloyDB.
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On Google Cloud, one managed option is agent platform memory bank.
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Memory bank is useful because it does more than store vectors.
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You can read past sessions, extract useful memories, and update those memories over time so
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if you use a plain vector store you usually build
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that extraction logic yourself if you use memory bank
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that part is managed for you
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but the lesson is not you must use memory bank the lesson is understanding goes in a system
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that can search by meaning now here's a mistake many people make they hear semantic memory
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and they put everything into the vector store.
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Facts, preferences, calendar events, everything.
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That sounds simple, but it creates a real problem.
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A vector store is designed for similarity.
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Similarity is great when the question is fuzzy, but similarity is dangerous.
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when the answer must be exact.
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If the user asks, what did I buy?
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The agent should not guess from a similar memory.
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It should curate the database.
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If the user asks, what do I usually like for dinners?
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Then semantic memory makes sense.
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That is a meaning -based question.
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So the rule is, structured facts goes in database like Cloud SQL.
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Unstructured understanding goes in a vector store.
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And there is one more category people often forget, raw perception.
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The camera frames, the audio waveform, the live stream itself, that does not usually belong in memory.
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Your agent does not need to permanently remember every pixel or every sound wave.
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It needs the meaning you extract from them.
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For example, she is wearing that black hat again.
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Or that you just said this dinner is formal.
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Those meanings can become memory.
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The raw video and audio are separate storage decisions.
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You might keep them for replay, audit, debugging, or training data.
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But that is not the same thing as agent memory.
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Agent memory should keep what matters, not every raw signal that pass through the app.
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So here is a second takeaway.
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Facts go in structured memory, like classical.
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Understanding goes in a vector store, like agent, platform, memory, bank.
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Role perception usually stays out of memory, unless you have a separate reason to store the media itself.
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Alright, let's make this concrete.
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How does the agent actually remember?
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There are two important moments, the start of a session and the end of a session.
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At the start, the agent loads memory before the conversation really begins.
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It reads structured facts from the database, for example, upcoming occasions, wishlist items, profile details.
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Then it searches the vector store for a relevant understanding, for example, style preference, comfort constraints, favorite colors from past conversations.
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Then the agent gets both kinds of context, so when the user starts talking, the agent is not starting from zero,
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it already has the facts and it already has a meaning.
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That is why the code open works.
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The agent can say, you have that dinner on Thursday from the structure database
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and I know you like to keep it simple from semantic memory.
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One answer, two memory systems.
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Now let's talk about saving memory.
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Facts should usually be saved immediately.
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If the user says I Add that Blazor to my wish list, the agent should write that to the database right away.
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That is a tool call.
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It is structured, it is exact, and the user expects it to be saved now.
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Understanding is different.
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You usually do not want to update someone's long -term identity from one random sentence.
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If I say, maybe I like yellow today, that does not mean yellow is now my whole style.
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Understanding needs judgment.
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It should come from stronger signals.
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Maybe the user explains why something matters.
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Maybe the same pattern repeats across sessions.
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That is how you keep memory from getting noisy.
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So instead of treating every sentence as a permanent preference, you let the memory series review the conversation and extract what is actually worth keeping.
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Now, when does the extraction happen?
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You have a choice.
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You can extract memory during the conversation, after each turn, or at interverse.
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Agent Platform Memory Bank supports this directly.
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You can trigger memory generation mid -session, not only at the end
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and extracting as you go has real advantages shorter exchanges often produce cleaner memories
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and if the user drops off early you have already saved what they said
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but there's a catch in a live multi -modal agent extraction
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is heavier than a database write a model has to read the conversation
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and infer meaning that takes time and a live voice agent is latency -sensitive,
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you cannot let a slow extraction step block the audio.
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So the rule is not always wait until the session ends.
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The rule is keep extraction off the live path.
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Run it asynchronously, after the turn or after the session, wherever it will not stall the conversation.
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And in this demo, the structured facts are written immediately during the conversation.
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The understanding is extracted off the hot path after the session and stored as semantic memory.
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That gives you two tracks, fast synchronous writes for facts, slower asynchronous extraction for understanding,
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and the next time the user comes back and the agent loads both again.
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That is the loop.
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Read at the start, write what changes, bring it forward next time.
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Here's the third takeaway.
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So both memory systems, when the session starts, save facts as soon as it happens,
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save understanding after you have enough context to know what is actually worth remembering.
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All right, let's recap.
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If you want your AI agent to remember users across sessions, do not start by dumping everything into one type of database.
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Start by asking, what kind of memory is this?
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There are two main kinds.
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Facts are exact.
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It looks them up.
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Understanding is meaning.
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You recall it.
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Facts belong in a structural database like classical.
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Understanding belongs in a vector store.
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Agent Platform Memory Bank is one managed option because it can help extract and maintain that semantic memory for you.
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But the architecture works beyond one product and raw camera frames or raw audio do not automatically become memory.
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You usually store the meaning you extract from them, not the raw signal itself.
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That is what turns a forgetful agent into an agent that can actually pick up where it left off.
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It knows the facts of your life and it understands what patterns that makes those facts useful.
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That's it.
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So what is the first thing you would want your agent to remember about you?
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Don't forget to leave that in the comments.
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In the next episode, we will give the agent eyes with real -time vision.
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All right, I will see you in the next episode.
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Bye.
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Thank you.
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Vocabulary and speaking notes for this lesson
This speaking lesson is built on the video “AI Agent Memory Architecture: Structured SQL, Semantic Vector Search & Memory Bank”. The speaker keeps coming back to these words: memory, agent, understanding, session, vector. This video has 173 sentences and 1681 words to shadow. The speech runs for 12:17. The speaker talks at a steady 137 words per minute, a comfortable pace for shadowing. 84% of the words are among the 3,000 most common in English; the rest is worth studying before you start.
Key vocabulary in this video
15 words from the video worth learning, with pronunciation and meaning:
| Word | Pronunciation | Meaning |
|---|---|---|
| vector noun | /ˈvɛktɚ/ | A directed quantity, one with both magnitude and direction; the signed difference between two points. |
| database noun | /ˈdeɪtəˌbeɪs/ | A collection of (usually) organized information in a regular structure, usually but not necessarily in a machine-readable format accessible by a computer. |
| extract noun | /ˈɛkstɹækt/ | Something that is extracted or drawn out. |
| extraction noun | /ɪkˈstɹækʃən/ | The act of extracting or the condition of being extracted. |
| audio adjective | /ˈɔ.di.oʊ/ | Focused on audible sound, as opposed to sight. |
| belong verb | /bɪˈlɒŋ/ | To have its proper place. |
| preference noun | /ˈpɹɛf(ə)ɹ(ə)ns/ | The selection of one thing or person over others (with the main adposition being "for" in relation to the thing or person, but possibly also "of") |
| calendar noun | /ˈkæl.ən.də/ | Any system by which time is divided into days, weeks, months, and years. |
| similarity noun | /ˌsɪm.ɪˈlæɹ.ɪ.ti/ | Closeness of appearance to something else. |
| lesson noun | /ˈlɛs.ən/ | A section of learning or teaching into which a wider learning content is divided. |
| architecture noun | /ˈɑː.kɪˌtɛk.t͡ʃə/ | The art and science of designing and managing the construction of buildings and other structures, particularly if they are well proportioned and decorated. |
| recall verb | /ɹɪˈkɔl/ | To withdraw, retract (one's words etc.); to revoke (an order). |
| classical adjective | /ˈklæs.ɪ.k(ə)l/ | Of or relating to the first class or rank, especially in literature or art. |
| perception noun | /pɚˈsɛpʃ(ə)n/ | The organisation, identification and interpretation of sensory information. |
| sensitive adjective | /ˈsɛn.sɪt.ɪv/ | Having the faculty of sensation; pertaining to the senses. |
Phrasal verbs you will hear
| Word | Pronunciation | Meaning |
|---|---|---|
| bring back verb | /ˈbɹɪŋ bæk/ | To fetch something. |
| come back verb | To return to a place. | |
| drop off verb | To fall asleep. | |
| leave off verb | To omit. | |
| look up verb | /ˌlʊk ˈʌp/ | To have better prospects. |
| pass through verb | To go through, to travel through, to transit or lie across a place or from one place to another. | |
| pick up verb | To lift; to grasp and raise. |
Sentences worth repeating
Short, complete lines from the video that you can reuse in everyday conversation:
- If the user asks, what did I buy?
- If the user asks, what do I usually like for dinners?
- Alright, let's make this concrete.
Pronunciation to watch
The speaker uses 3 contractions and reduced forms, such as don't, I'm, you're. Say them the short way, as you hear them.
- The “sh” and “zh” sounds: extraction /ɪkˈstɹækʃən/, perception /pɚˈsɛpʃ(ə)n/, occasion /əˈkeɪ̯ʒən/, structural /ˈstɹʌk(t͡)ʃəɹəl/, flashy /ˈflæʃi/
- Long words — get the stress right: similarity /ˌsɪm.ɪˈlæɹ.ɪ.ti/, architecture /ˈɑː.kɪˌtɛk.t͡ʃə/, permanently /ˈpɜː.mə.nənt.li/, asynchronous /eɪˈsɪŋ.kɹə.nəs/
How to practise with this video
- Listen to the whole video once without speaking and note the words you do not know.
- Shadow it sentence by sentence at normal speed, repeating each one until your rhythm matches the speaker.
- Record yourself and compare with the original, paying attention to words like vector, database, extract.
What is the Shadowing Technique?
Shadowing is a science-backed language learning technique originally developed for professional interpreter training and popularized by polyglot Dr. Alexander Arguelles. The method is simple but powerful: you listen to native English audio and immediately repeat it out loud — like a shadow following the speaker with just a 1–2 second delay. Unlike passive listening or grammar drills, shadowing forces your brain and mouth muscles to simultaneously process and reproduce real speech patterns. Research shows it significantly improves pronunciation accuracy, intonation, rhythm, connected speech, listening comprehension, and speaking fluency — making it one of the most effective methods for IELTS Speaking preparation and real-world English communication.