Prática de Shadowing: LangChain in 5 Minutes (Explained Clearly) - Aprenda a falar inglês com vídeo
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If you've started learning AI development, you've probably heard of Langchain.
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Some people say it's essential.
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Others say you don't need it anymore.
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So what exactly is Langchain?
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And more importantly, should you learn it?
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By the end of this video, you'll understand what Langchain is, why it was created, and when you should actually use it.
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Let's start with the problem.
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Imagine you're building an AI chatbot using GPT or Claude.
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At first, it's simple.
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The user asks a question, you send the prompt to the language model, the model generates an answer.
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Done.
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But real AI applications quickly become more complicated.
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What if the chatbot needs to answer questions from your company documents?
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What if it needs to search a database?
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What if it should remember previous conversations?
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or call an external API.
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or use different AI models depending on the task.
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Soon, your application contains lots of custom code just to connect all these pieces together.
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This is exactly the problem Langchain was built to solve.
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Langchain is an open source framework that helps developers build applications powered by large language models.
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Instead of writing everything from scratch, Langchain provides reusable building blocks that connect language models,
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prompts, memory, documents, databases, and external tools.
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You can think of it as the glue that connects all these components together, Now let's look at its most important concepts.
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The first is "Prompt Templates".
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Normally, you might write a prompt directly inside your code.
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but in real applications, prompts often contain dynamic values.
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For example, Give me the top programming languages to learn in year within parenthesis.
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Instead of manually building strings every time, Langchain lets you create reusable templates and simply replace the variables when the application runs.
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This makes prompts much cleaner and easier to maintain.
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The next concept is chains.
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As the name suggests, a chain is simply a sequence of steps.
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Imagine a user asks a question.
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First, your application formats the prompt.
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Then it sends the prompt to the language model.
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Finally, it formats the response before returning it to the user.
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Instead of writing separate code for every step, Langchain lets you connect them into one workflow.
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and because every step is modular, it's easy to replace or extend later.
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Another powerful feature is document retrieval.
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Large language models don't know your private company documents.
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They only know what they were trained on.
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So how does an AI answer questions about your own PDFs?
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policies, or internal documentation.
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The application first searches your documents to find the most relevant information.
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It then sends that information to the language model along with the user's question.
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Finally, the model generates an answer using both the question and the retrieved documents.
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Langchain provides many of the components needed to build this type of retrieval augmented generation, or RAG, application.
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Another important feature is memory.
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Imagine asking, What is Docker?
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then a few seconds later asking, How is it different from Kubernetes?
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Without memory, the AI wouldn't know what "it" refers to.
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Langchain helps applications keep track of previous conversations so responses remain consistent and contextual.
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The last major concept is agents.
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Unlike a normal workflow that always follows the same steps, an agent can decide what to do next.
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For example, suppose you ask: Check today's weather and send me an email if it's going to rain.
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The AI might first call a weather API, then analyze the forecast.
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Finally, call an email service.
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Instead of following a fixed sequence, the agent chooses which tools to use based on your request.
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This makes AI applications much more flexible.
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So should you always use Langchain?
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Not necessarily.
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If your application simply sends one prompt to ChatGPT and displays the answer, Using the OpenAI or Anthropic SDK directly is usually simpler.
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But when your application needs prompt templates, Document Retrieval "Conversation Memory," Multiple AI Models tool calling,
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or complex workflows Langchain can save a lot of development time.
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Let's quickly recap.
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LangChain is an open -source framework for building AI applications.
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Prompt templates create reusable prompts.
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Chains connect multiple processing steps.
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Retrieval helps AI search your own documents.
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Memory maintains conversation context.
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Agents allow AI to use external tools and make decisions.
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The language model is still the brain.
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LangChain simply provides the framework that connects everything together.
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If you're serious about building production AI applications, understanding Langchain is definitely worth your time, because many modern AI systems are built using these same concepts,
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even if they don't use Langchain itself.
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Vocabulário e dicas de fala para esta lição
Este vídeo tem 74 frases e 752 palavras para praticar shadowing. A fala dura 5:04. O falante mantém um ritmo estável de cerca de 148 palavras por minuto, confortável para o shadowing. Apenas 80% das palavras estão entre as 3.000 mais comuns do inglês, por isso o vocabulário é exigente.
Vocabulário principal deste vídeo
15 palavras do vídeo que vale a pena aprender, com pronúncia e significado:
| Palavra | Pronúncia | Significado |
|---|---|---|
| prompt verbo | /pɹɑmpt/ | incitar, impelir |
| connect verbo | /kəˈnɛkt/ | conectar |
| template substantivo | /ˈtɛm.plɪt/ | modelo, predefinição |
| framework substantivo | /ˈfɹeɪm.wɜːk/ | esqueleto, estrutura |
| format substantivo | /ˈfɔːɹ.mæt/ | formato |
| sequence substantivo | /ˈsiː.kwəns/ | sequência |
| component substantivo | /kəmˈpoʊ.nənt/ | componente |
| database substantivo | /ˈdeɪtəˌbeɪs/ | banco de dados, base de dados |
| generate verbo | /ˈd͡ʒɛn.ə.ɹeɪt/ | gerar |
| consistent adjetivo | /kənˈsɪs.tənt/ | consistente |
| necessarily advérbio | /ˌnɛs.əˈsɛɹ.ə.li/ | necessariamente |
| complicated adjetivo | /ˈkɑm.plɪˌkeɪ.tɪd/ | complicado |
| refer verbo | /ɹɪˈfɜː/ | encaminhar |
| custom substantivo | /ˈkʌstəm/ | costume, hábito |
| solve verbo | /sɒlv/ | resolver, solucionar |
Pronúncia para ficar de olho
O falante usa 9 contrações e formas reduzidas, como don't, you're, you've. Diga-as na forma curta, do jeito que você ouve.
- Palavras longas — acerte a sílaba tônica: necessarily /ˌnɛs.əˈsɛɹ.ə.li/, complicated /ˈkɑm.plɪˌkeɪ.tɪd/, developer /dɪˈvɛləpɚ/, importantly /ɪmˈpɔɹ.tənt.li/, documentation /ˌdɑkjəmɛnˈteɪʃən/
Como praticar com este vídeo
- Ouça o vídeo inteiro uma vez sem falar e anote as palavras que você não conhece.
- Faça shadowing frase por frase na velocidade normal, repetindo cada uma até o seu ritmo ficar igual ao do falante.
- Grave a sua voz e compare com o original, prestando atenção a palavras como prompt, connect, template.
O que é a Técnica de Shadowing?
Shadowing é uma técnica de aprendizado de idiomas com base científica, originalmente desenvolvida para o treinamento de intérpretes profissionais. O método é simples, mas poderoso: você ouve áudio em inglês nativo e repete imediatamente em voz alta — como uma sombra seguindo o falante com 1-2 segundos de atraso. Pesquisas mostram melhora significativa na precisão da pronúncia, entonação, ritmo, sons conectados, compreensão auditiva e fluência na fala.