Shadowing Practice: RAG's Evolution: From Simple Retrieval to Agentic AI - Learn English Speaking with Video
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We've all had this experience.
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You search for something, you get thousands of results, and somehow none of them are what you wanted.
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Well, what if I told you search engines don't actually understand your questions?
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At least, they didn't used to.
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From simple keyword search to present -day agentic rag, information retrieval has seen an evolution.
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And search engines didn't get smarter overnight.
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They grew up one step at a time.
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Let's start from the beginning.
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The earliest search systems were designed around the question of, where does this word appear?
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Documents were indexed using what's called inverted indices, aka a mapping of keywords to documents.
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When a user asks a question,
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the search system will look up these words and quickly return the matching documents.
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These documents may then be ranked using TF -IDF or BM -25 to measure how important or frequent different terms were.
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This powerful keyword matching approach still powers a lot of the internet today.
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But there's a fundamental limitation.
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It doesn't understand language.
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It treats words as symbols, not meaning.
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Synonyms, ambiguity, and any complex intents were essentially invisible.
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For example, is the search help
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Python related to coding or did I just get a pet snake?
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It was on the user to be asking the right questions with the exact right words.
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The next major leap was semantic search.
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Instead of treating text as words, we began representing them as language.
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This is done using vectors or high dimensional number representations that can understand meaning.
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For example, coffee might be represented as 010 versus house might be represented as 100.
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These embeddings don't just come out of nowhere.
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They are learned by large neural networks trained on massive text corpora.
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By encountering words in context, Over time, these similar concepts will end up close together, even if they use different words.
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If this is coffee, maybe espresso is represented here.
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Very close in concept to coffee, but not anywhere close to house.
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Semantic search turns your words into a kind of map, so the system knows espresso and coffee are pointing to a very similar place.
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It's essentially your friend who knows what you mean even if you don't say it perfectly every time.
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This allowed search systems to understand intent.
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Even if the exact keywords were not used, you could still find relevant documents.
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And this didn't replace keyword search.
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It actually complemented it.
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Hybrid systems began to emerge, bridging the precision of keyword search with semantic recall.
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For the first time, instead of just matching text, search was able to approximate understanding.
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Then the world shifted.
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Large language models were born.
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These are models trained on a large corpora of text to learn patterns in the data.
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LLMs don't retrieve facts. When prompted, they will predict the most likely next token
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or words for an answer based on those patterns that they learned from the training data.
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The user asks a question to the LLM and it will return a text answer.
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These are super powerful and revolutionized the business world.
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However, they had a problem.
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LLMs only use specific knowledge they learned during a long and expensive training process.
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Realistically, that means any knowledge is locked to only the documents
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that that specific LLM was trained on before a certain point in time.
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LLMs don't know today's information and certainly don't know your specific documents.
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So what's the solution?
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Well, it's actually search.
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Retrieval Augmented Generation or RAG was born.
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The idea is very simple.
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The user asks a question.
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The system does a search for relevant documents using an external knowledge base.
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This retrieval is used to augment
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The LLM's prompt and a final answer is generated.
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This gave LLMs a form of external memory.
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Now they could cite sources, adapt to new information, and even operate in specialized domains without the costly retraining.
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These original RAG pipelines were very linear.
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Documents were embedded offline into these vector databases.
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They were retrieved once at query time and passed straight into the model.
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It was simple but effective.
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This massive improvement significantly dropped hallucinations and enabled LLM adoption across a multitude of new domains.
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But traditional RAG is nowhere near perfect.
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It cannot adapt to new scenarios.
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And suddenly, we are back at the problem of traditional search.
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The answer is only as good as the search itself.
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Within such a short period, countless advancements were made to RAG, developing the simple concept into a sophisticated power to be reckoned with.
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Instead of a single retrieval step, pipelines added re -rankers to reorder results to be more relevant.
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User queries were rewritten or expanded upon to improve recall.
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Similar to before, hybrid retrieval became the norm,
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leveraging the precision of keyword search with semantic vector search.
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These systems were far more accurate, but still fundamentally static.
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The pipeline was predetermined, and retrieval was smarter, but still not intelligent.
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Enter the next disruptor, agents.
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Agents are systems that use LLMs and tools to perform tasks autonomously.
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Suddenly, we shifted from simple pipelines to complex decision -making systems.
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Agents have a variety of tools, such as LLMs, memory, planning,
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critics, tactics, retrievers, and many more.
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Agents have become autonomous decision makers planning and executing complex tasks.
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Now, instead of linear rag retrieval, when the user asks a question, an AI agent will decide whether retrieval is needed,
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where to search, what questions should be asked, when enough information is obtained, and then generate a final answer.
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Agents can compare sources, validate claims, refine queries, and iterate.
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It can invoke APIs, pull data from many knowledge bases, and incorporate multimodal data.
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Retrieval is no longer fixed.
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It's a tool invoked as part of reasoning.
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This opens up a world of possibilities.
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Now, agentic rag systems are capable of multi -step research, cross -document synthesis, and general adaptive behavior.
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The system doesn't just answer questions, it reasons and figures out how to answer them.
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From simple search to current agentic rag, we have learned time and time again that the next big step isn't better answers.
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It's systems that know how to find them.
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And the hardest part of AI isn't generation, it's deciding what to look at.
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Woordenschat en spreektips bij deze les
Deze spreekles is gebaseerd op de video “RAG's Evolution: From Simple Retrieval to Agentic AI”. Deze woorden komen het vaakst terug: search, document, Llm, retrieval, word. Deze video bevat 79 zinnen en 1029 woorden om na te spreken. Het gesproken deel duurt 8:36. De spreker praat in een gelijkmatig tempo van ongeveer 120 woorden per minuut, prettig om te shadowen. Slechts 77% van de woorden hoort bij de 3.000 meest gebruikte Engelse woorden, dus de woordenschat is pittig.
Belangrijke woorden in deze video
15 woorden uit de video die het leren waard zijn, met uitspraak en betekenis:
| Woord | Uitspraak | Betekenis |
|---|---|---|
| pipeline zelfstandig naamwoord | /ˈpaɪpˌlaɪn/ | pijpleiding, pijplijn |
| vector zelfstandig naamwoord | /ˈvɛktɚ/ | vector |
| query zelfstandig naamwoord | /ˈkwɪɹ.i/ | vraag, verzoek |
| essentially bijwoord | /ɪˈsɛnʃəli/ | essentieel |
| nowhere bijwoord | /ˈnoʊ.(h)wɛɹ/ | nergens, nergens heen |
| domain zelfstandig naamwoord | /ˌdəʊˈmeɪ̯n/ | domein |
| intent zelfstandig naamwoord | /ɪnˈtɛnt/ | bedoeling |
| linear bijvoeglijk naamwoord | /ˈlɪn.i.əɹ/ | lineair |
| hybrid zelfstandig naamwoord | /ˈhaɪ.bɹɪd/ | kruising, hybride |
| precision zelfstandig naamwoord | /pɹɪˈsɪʒ.ən/ | precisie |
| adapt werkwoord | /əˈdæpt/ | aanpassen |
| prompt werkwoord | /pɹɑmpt/ | aansporen, aanmoedigen |
| retrieve werkwoord | /ɹɪˈtɹiːv/ | terugbrengen, terugkrijgen |
| corpus zelfstandig naamwoord | /ˈkɔɹpəs/ | corpus |
| compare werkwoord | /kəmˈpɛɚ/ | vergelijken |
Phrasal verbs die je zult horen
| Woord | Uitspraak | Betekenis |
|---|---|---|
| end up werkwoord | belanden, terechtkomen | |
| figure out werkwoord | uitvogelen, er achter komen | |
| grow up werkwoord | opgroeien | |
| look up werkwoord | /ˌlʊk ˈʌp/ | opkijken |
Zinnen om te herhalen
Korte, volledige zinnen uit de video die je in alledaagse gesprekken kunt gebruiken:
- We've all had this experience.
- Let's start from the beginning.
- Related to coding, or did I just get a pet snake?
Grammatica in deze video
De structuren die de spreker het meest gebruikt, met de exacte woorden uit de video:
| Structuur | In de video |
|---|---|
| Lijdende vorm be + voltooid deelwoord — het gaat om wat er gebeurt, niet om wie het doet | were designed · were indexed · be ranked |
| Present perfect have/has + voltooid deelwoord — iets uit het verleden dat nu nog telt | has seen · have become · have learned |
Uitspraak om op te letten
De spreker gebruikt 14 samentrekkingen en verkorte vormen, zoals don't, didn't, doesn't. Spreek ze kort uit, zoals je ze hoort.
- De klanken “sh” en “zh”: essentially /ɪˈsɛnʃəli/, precision /pɹɪˈsɪʒ.ən/, evolution /ˌiːvəˈluːʃ(ə)n/, representation /ˌɹɛp.ɹə.zɛnˈteɪ.ʃən/, adoption /əˈdɑp.ʃən/
- Lange woorden — let op de klemtoon: essentially /ɪˈsɛnʃəli/, evolution /ˌiːvəˈluːʃ(ə)n/, representation /ˌɹɛp.ɹə.zɛnˈteɪ.ʃən/, fundamental /ˌfʌn.dəˈmɛn.təl/, intelligent /ɪnˈtɛlɪd͡ʒənt/
Klanken die Nederlandstaligen lastig vinden:
- Stemhebbende eindmedeklinker — /b/, /d/, /g/, /z/, /v/ niet verscherpen: hybrid /ˈhaɪ.bɹɪd/, retrieve /ɹɪˈtɹiːv/, expand /ɪkˈspænd/, emerge /ɪˈmɝd͡ʒ/, leverage /ˈlɛv.(ə.)ɹɪd͡ʒ/
- /æ/ — opener dan de Nederlandse “e”: adapt /əˈdæpt/, rank /ˈɹæŋk/, expand /ɪkˈspænd/, static /ˈstæt.ɪk/, advancement /ædˈvæns.mənt/
- /g/ — een harde plofklank, geen Nederlandse “g”: augment /ɔɡˈmɛnt/, ambiguity /æm.bɪˈɡjuː.ɪ.ti/
Zo oefen je met deze video
- Luister de hele video één keer zonder te spreken en noteer de woorden die je niet kent.
- Spreek zin voor zin na op normale snelheid en herhaal elke zin tot je ritme gelijk is aan dat van de spreker.
- Neem jezelf op en vergelijk met het origineel; let daarbij op woorden als pipeline, vector, query.
Wat is de Shadowing-techniek?
Shadowing is een wetenschappelijk onderbouwde taalleermethode die oorspronkelijk is ontwikkeld voor professionele tolkentraining en gepopulariseerd door polyglot Dr. Alexander Arguelles. De methode is eenvoudig maar krachtig: je luistert naar native Engelse audio en herhaalt het onmiddellijk hardop — als een schaduw die de spreker volgt met slechts 1–2 seconden vertraging. In tegenstelling tot passief luisteren of grammaticadrills, dwingt shadowing je hersenen en mondspieren om echte spraakpatronen tegelijkertijd te verwerken en te reproduceren. Onderzoek toont aan dat het de uitspraaknauwkeurigheid, intonatie, ritme, verbonden spraak, luisterbegrip en spreekvaardigheid aanzienlijk verbetert — waardoor het een van de meest effectieve methoden is voor IELTS Speaking-voorbereiding en echte Engelse communicatie.