-
GenAI – What Is Fine – Tuning LLM Models ?
GenAI – What Is Fine Tuning LLM Models ? Table Of Contents: What Is Fine-Tuning in LLM Models. Why We Need To Fine Tune LLM Models ? Techniques Available To Fine Tune LLM Models. Comparison Table. Real World Example. Tools & Libraries For Fine Tuning. Summary. (1) What Is Fine Tuning LLM Models ? (2) Why We Need Fine Tuning LLM Models ? (3) Techniques Available To Fine Tune LLM Models. (4) Comparison Table (5) Real-World Example (6) Tools & Libraries for Fine-Tuning (7) Summary
-
GenAI – Types Of Prompting Techniques
GenAI – Types Of Prompting Techniques. Table Of Contents: Zero Short Prompting. Few Short Prompting. Chain Of Thoughts Prompting. Role Prompting. System Prompting. Instruction Tuning. Self Consistency Prompting. (1) Zero Shot Prompting Example – 1 Example – 2 (2) Few Shot Prompting Example – 1 Example – 2 Example – 3 Example – 4 Example – 5 Example – 6 (3) Chain Of Thoughts Prompting Example – 1 Example – 2 Example – 3 Example – 4 Example – 5 How You Will Pass The Questins To The LLM In Chain Of Thoughts ? (4) Role Prompting You are a
-
GenAI – What Is Prompt Engineering ?
GenAI – What Is Prompt Engineering ? Table Of Contents: What is Prompt Engineering? Why is Prompt Engineering Important? Components of a Good Prompt Types of Prompting Techniques How to Write Powerful Prompts (Pro Tips) Real Prompt Examples Advanced Tools That Help with Prompt Engineering In Simple Words Modern Prompt Templates (1) What Is Prompt Engineering ? Example -1 : Text Summarization Example -2 : Email Writing Task Example -3 : Coding Help Example -4 : Customer Support ChatbotCustomer Support Chatbot Example -5 : Content Generation (Social Media Post) (2) Why Prompt Engineering Is Important ? (3) Components Of Good
-
GenAI – Techniques To Train The LLM On Domain Specific Data .
GenAI – Techniques To Train The LLM On Domain Specific Data . Table Of Contents: Prompt Engineering. Instruction Tuning. Retrieval Augmented Generation(RAG) Parameter Efficient Finetuning(PEFT) Full Fine Tuning. Continued Pretraining(DAPT/TAPT). Reinforcement Learning. Knowledge Injection. (1) Prompt Engineering. (2) Instruction Tuning (3) RAG (Retrieval-Augmented Generation) (4) Parameter Efficient Fine-Tuning. (5) Full Fine Tuning (6) Continued Pretraining(DAPT/TAPT). (7) Reinforcement Learning (8) Knowledge Injection
-
GenAI – What Is Hallucination In GenAI ?
GenAI – What Is Hallucination In GenAI ? Table Of Contents: What Is Hallucination In GenAI ? Examples Of GenAI Model Hallucination . Why Does Hallucination Happen ? How To Fix Hallucination ? (1) What Is Hallucination In GenAI ? (2) Examples Of GenAI Model Hallucination. (3) Why Does Hallucination Happen ? (4) How To Fix Hallucination In GenAI ?
-
GenAI – How To Train LLM On Domain Specific Data ?
GenAI – Why We Need To Train Our LLM Model On Domain Specific Data ? Table Of Contents: Why We Need To Train Our LLM Model On Domain Specific Data ? What Is Hallucination In LLM ? What Techniques Are There To Train The LLM Model On Domain Specific Data ? What Is Fine Tuning LLM Model ? What Is RAG Based Training LLM Model ? (1) Why We Need To Train Our LLM Model On Domain Specific Data ?
-
GenAI – Steps Involved Building LLM Model.
GenAI – Steps Involved Building LLM Models Table Of Contents: Define The Problem Statement . Select The LLM . Prepare the Data (Optional, if fine-tuning or custom RAG) . Design the Interaction (Prompt Engineering) . (Optional) Fine-tune / Adapt the Model . Set Up the Backend (Model Serving) . Build the Frontend (User Interface) . Integrate Memory, Tools, or Search (Advanced Features) . Test and Evaluate the Application . Deploy to Production . (1) Define The Problem Statement (2) Select The LLM (3) Prepare the Data (Optional, if fine-tuning or custom RAG) (4) Design the Interaction (Prompt Engineering) (5) (Optional)
-
GenAI – Introduction To Generative AI.
GenAI – GenAI Roadmap For Beginners. Table Of Contents: What Is Generative AI? Examples Of Generative AI. Generative AI Tools. Best Open-Source Text Generation Models Best Closed-Source (Proprietary) Text Generation Models (1) What Is Generative AI ? (2) Examples Of Generative AI. (3) Generative AI Tools. (4) Best Open-Source Text Generation Models (5) Best Closed-Source (Proprietary) Text Generation Models
-
PySpark – PySpark Graphs
PySpark – PySpark GraphX / GraphFrames Table Of Contents: What is a Graph in PySpark? Example Of PySpark Graph. Why Use Graphs in PySpark? Where Does The Pyspark Graph Is Used In Real Life? (1) What is a Graph in PySpark? (2) Example Of PySpark Graph. from graphframes import GraphFrame g = GraphFrame(vertices, edges) (3) Why Use Graphs in PySpark? (4) PySpark Real Life Examples
-
PySpark – PySpark Streaming
PySpark – PySpark Streaming Table Of Contents: What is Spark Streaming? What is Structured Streaming? Key Concepts Example Code Spark Streaming vs. Structured Streaming Use Cases (1) What Is Spark Streaming ? (2) What Is Structured Streaming ? (3) Key Concepts: (4) Example Code from pyspark.sql import SparkSession spark = SparkSession.builder.appName("StructuredStreamingExample").getOrCreate() # Read stream from a socket source df = spark.readStream.format("socket").option("host", "localhost").option("port", 9999).load() # Word count logic words = df.selectExpr("explode(split(value, ' ')) as word") word_counts = words.groupBy("word").count() # Write the results to the console query = word_counts.writeStream.outputMode("complete").format("console").start() query.awaitTermination() (5) Spark Streaming vs. Structured Streaming (6) Use Cases
