AI Engineering Bootcamp: Building AI Applications (LangChain, LLM APIs + more)

Name: AI Engineering Bootcamp: Building AI Applications (LangChain, LLM APIs + more)
Price: 9 USD
Availability: InStock

18h 33m 41s

English

Paid

September 26, 2025

Course description

This course is your practical path to the profession of a generative AI engineer: not just using technologies, but creating them.

First, you will enhance your Python skills: structuring modular code, working with APIs, and data processing. Then, you'll dive into the basics of large language models (LLMs) - how they are structured, trained, and how to interact with them effectively through advanced prompt engineering.

Next, practice. You will learn to create real AI applications based on OpenAI and Gemini API, including chat systems, working with images and audio. You'll master LangChain for building agents and prompt chains, as well as LangGraph for managing multi-step processes. You will add memory to your applications using embeddings and vector databases, and learn to debug and scale production systems with LangSmith.

Throughout the course, you will develop chatbots, intelligent tools for working with images, search Q&A systems, and much more. The final project will combine all the skills: you will create a research agent that uses search, tools, and reasoning to generate high-quality reviews of real data.

This course is a path from AI experiments to a real engineering approach.

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#1: AI Engineering Bootcamp: Building AI Applications with LLM APIs, LangChain + much more!Using Jupyter Notebook

All Course Lessons (181)

#	Lesson Title	Duration
1	AI Engineering Bootcamp: Building AI Applications with LLM APIs, LangChain + much more!Using Jupyter Notebook Demo	02:02
2	Using Jupyter Notebook	09:24
3	Using Virtual Environments (venv)	10:07
4	Getting Started with the requests and httpx Libraries in Python	08:50
5	Handling HTTP Errors	04:39
6	Managing HTTP Authentication and Headers (OpenAI API)	09:59
7	Setting Up the Environment: Jupyter Notebook and Pandas	03:55
8	Introduction to Pandas: Series and DataFrames	06:09
9	Importing and Exporting Data: Working with CSV Files	06:38
10	Exporting Data to Different Formats: Excel, JSON, SQL, YAML	07:47
11	Modifying Data: Adding and Dropping Columns and Rows	06:05
12	Accessing Data: Using df.iloc[] and df.loc[]	05:43
13	Sampling and Previewing Data: Using df.sample() and df.head()	06:15
14	Filtering Data: Masks and pandas.Series.between()	07:15
15	Sorting Data: Understanding Pandas Sorting Methods	07:11
16	Handling Missing Data	04:44
17	Aggregations and Grouping Data	04:54
18	Project: Analyzing Website Traffic Data	04:33
19	Time Series Data Manipulation in Pandas	06:59
20	Foundations of LLMs and Generative AI	08:32
21	Tokens, Context Windows and Cost	05:26
22	Exploring LLM APIs: AI as a Service	09:23
23	OpenAI Playground, Google AI Studio, and Anthropic Workbench	06:06
24	Challenges and Limitations of LLMs	09:03
25	The State of AI: Present and Future вЂ“ The Good and the Bad	10:06
26	Pretraining Data (Internet)	06:41
27	Tokenization	06:07
28	Training the Neural Network	09:26
29	Post-Training: Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL)	08:26
30	Reinforcement Learning (RL)	05:30
31	Becoming Better than Humans: AGI and ASI with RL	07:32
32	Reinforcement Learning with Human Feedback (RLHF)	06:23
33	How to Deal With Hallucinations	07:37
34	Using Tools: Internet Search, Interpreter, and Deep Search	07:49
35	Big Ideas Recap (Core Summary)	09:51
36	Authenticating to OpenAI using Python Dotenv	08:17
37	Chat Completions Endpoint	06:58
38	Developer Message	04:31
39	Streaming API Responses	04:31
40	Using Local Base64 Images as Input	06:44
41	Using Online Images as Input	02:05
42	Chat Completion API Parameters: Temperature and Seed	06:14
43	Chat Completion API Parameters: Top P, Max_Tokens, Penalties	09:50
44	Diving into OpenAIвЂ™s Reasoning Models (o1 and o3)	07:56
45	Best Practices for Prompting Reasoning Models	05:26
46	Transcriptions with Whisper	05:48
47	Translations with Whisper	03:12
48	Text-to-Speech (TTS) API	07:03
49	Generating Original Images Using the DALL-E 3	10:50
50	Creating Variations of Images with DALL-E	03:05
51	Editing Images with DALL-E	05:40
52	Intro to Prompt Engineering	02:41
53	Tactic 1: Position Instruction Clearly with Delimiters	04:13
54	Tactic 2: Provide Detailed Instructions for the Context	06:38
55	Tactic 3: Use the Rich Text Format (RTF)	07:46
56	Tactic 4: Few Shot Prompting	08:13
57	Tactic 5: Specify the Steps Required to Complete a Task	05:17
58	Tactic 6: Give Models Time to Think	02:13
59	Other Tactics and Principles for Better Prompting	05:38
60	Avoid Hallucinations Using Guarding	03:07
61	Summary	02:07
62	Project Introduction	02:32
63	Creating a Daily Meal Plan Using OpenAI API	05:39
64	Creating the Prompt	08:43
65	Running the Program	03:24
66	Generating Original Images for the Recipes using DALL-E	11:54
67	Narrate the Meals using the Text-to-Speech Model	10:24
68	Setting Up the Python SDK and Authenticating for Gemini API	09:51
69	Generating Text From Text Prompts	04:15
70	Streaming Gemini Responses	02:59
71	Generating Text From Images	05:49
72	Gemini API Generation Parameters: Controlling How the Model Generates Responses	06:12
73	Gemini API Generation Parameters Explained	10:14
74	Building Chat Conversations	07:54
75	Project: Building a Conversational Agent Using Gemini Pro	07:19
76	System Instructions	05:43
77	The File API: Prompting with Media Files	06:09
78	Tokens	06:42
79	Prompting with Audio	04:21
80	Project Requirements	05:54
81	Building the Application	05:23
82	Testing the Application	01:49
83	Streamlit: Transform Your Jupyter Notebooks into Interactive Web Apps	02:49
84	Creating the Web App Layout With Streamlit	11:20
85	Saving and Displaying the History Using the Streamlit Session State	05:20
86	Exercise: Imposter Syndrome	02:57
87	Project Introduction	00:57
88	Getting Images Using a Generator	06:18
89	Renaming Images Using Gemini	09:35
90	LangChain Demo	05:06
91	Introduction to LangChain	05:10
92	Working with the OpenAI Models	08:43
93	Caching LLM Responses	04:57
94	LLM Streaming	02:58
95	Prompt Templates	05:36
96	ChatPromptTemplate	05:55
97	Understanding Chains	07:48
98	Installing the Python Libraries for Gemini and Authenticating to Gemini	04:31
99	Integrating Gemini with LangChain	06:02
100	Using a System Prompt and Enabling Streaming	06:32
101	Multimodal AI With Gemini	14:13
102	LangChain Tools: DuckDuckGo and Wikipedia	11:08
103	Creating a React Agent	13:30
104	Testing the React Agent	04:50
105	Intro to OpenAI's Text Embeddings	03:16
106	Generating Simple Embeddings	05:54
107	Embedding the Dataset for Similarity Searches	04:52
108	Estimating Embedding Costs With Tiktoken	05:12
109	Performing Semantic Searches	07:05
110	Project Introduction	06:09
111	Loading Your Custom (Private) PDF Documents	07:28
112	Loading Different Document Formats	05:13
113	Public and Private Service Loaders	04:38
114	Chunking Strategies and Splitting the Documents	06:39
115	Intro to Vector Stores and Authenticating to Pinecone	09:02
116	Working with Pinecone Indexes	09:32
117	Working with Vectors	08:43
118	Pinecone Namespaces	06:44
119	Embedding and Uploading to a Vector Database (Pinecone)	13:53
120	Asking and Getting Answers	11:43
121	Using Chroma as a Vector DB	11:11
122	Adding Memory to the RAG System (Chat History)	09:26
123	Using a Custom Prompt	08:10
124	Introduction to Agents and ReAct	04:20
125	Creating the Agent Class	02:42
126	Creating the ReAct Prompt	02:31
127	Creating the Tools	02:41
128	Testing the Agent	06:06
129	Automating the Agent	07:01
130	LangGraph Concepts and Core Components	05:43
131	Building a Chatbot	05:30
132	Visualizing the Graph	02:13
133	Running the Chatbot	01:32
134	Tavily AI	08:29
135	Enhancing the ChatBot with Tools	08:17
136	Adding Memory to the Chatbot	07:06
137	Intro to Reflection	02:14
138	Generate	04:16
139	Reflect and Repeat	02:33
140	Define the Graph - Part 1	03:44
141	Define the Graph - Part 2	02:49
142	Running the App	03:55
143	Intro to LangSmith	03:29
144	Setting Up LangSmith	01:55
145	Tracing with LangSmith	06:17
146	Tracing the Reflective Agentic App	03:51
147	Project Overview	01:48
148	Defining the Agent State and the Prompts	07:39
149	Implementing Agents and Nodes	09:39
150	Defining the Conditional Edge	01:27
151	Defining the Graph	04:25
152	Running the App	04:07
153	Tracing the App with LangSmith	02:51
154	Note	02:16
155	Application Overview	03:34
156	Extracting Data from ArXiv with Pandas	12:44
157	Downloading Research Papers	04:53
158	Loading, Splitting and Expanding Data	09:54
159	Building a Knowledge Base for RAG	05:35
160	Creating a Pinecone Index	07:17
161	Loading the Knowledge Base and Deploying to Pinecone	05:04
162	Developing Custom Tools	05:13
163	Implementing the ArXiv Fetch Tool	08:01
164	Unlocking Web Search with Google SerpAPI	03:29
165	Building Google SerpAPI Tools	04:26
166	Creating RAG Tools	06:20
167	Implementing the Final Answer Generation Tool	02:18
168	06_14 Initializing the Oracle LLM	11:02
169	Testing the Ecosystem	03:33
170	Building a Decision-Making Pipeline	08:34
171	Defining the Agent State	03:25
172	Defining the Graph	06:36
173	Generating Reports	04:27
174	Building the Final Research Report	05:20
175	Concluding the Project	06:23
176	Understanding Python Modules	06:17
177	The OS Module	07:57
178	Advanced Import Techniques and Best Practices	04:11
179	Using __name__ == '__main__' for Modular and Reusable Code	06:24
180	Mastering Python Package Management with pip	08:35
181	Thank You!	01:18

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