lifehack.guide

When responding to users, GPTs often use markdown to format the text in a structured and visually appealing way. Markdown is a lightweight markup language that allows for easy formatting of text, including headers, lists, links, and more. If you'd like to learn more about markdown and how to use it, I'd recommend checking out the Markdown Guide at 

https://www.markdownguide.org.

When using the Template Pattern, you can define the formatting of your desired output using markdown. Format of the Template Pattern

To use this pattern, your prompt should make the following fundamental contextual statements:

• I am going to provide a template for your output or I want you to produce your output using this template

• X or <X> is my placeholder for content (optional)

• Try to fit the output into one or more of the placeholders that I list (optional)

• Please preserve the formatting and overall template that I provide (optional)

• This is the template: PATTERN with PLACEHOLDERS

You will need to replace "X" with an appropriate placeholder, such as "CAPITALIZED WORDS" or "<PLACEHOLDER>". You will then need to specify a pattern to fill in, such as "Dear <FULL NAME>" or "NAME, TITLE, COMPANY".

Examples:

• Create a random strength workout for me today with complementary exercises. I am going to provide a template for your output . CAPITALIZED WORDS are my placeholders for content. Try to fit the output into one or more of the placeholders that I list. Please preserve the formatting and overall template that I provide. This is the template: NAME, REPS @ SETS, MUSCLE GROUPS WORKED, DIFFICULTY SCALE 1-5, FORM NOTES

• Please create a grocery list for me to cook macaroni and cheese from scratch, garlic bread, and marinara sauce from scratch. I am going to provide a template for your output . <placeholder> are my placeholders for content. Try to fit the output into one or more of the placeholders that I list. Please preserve the formatting and overall template that I provide. This is the template: Aisle <name of aisle>: <item needed from aisle>, <qty> (<dish(es) used in>

Markdown formatting:

Getting  Footnotes in Markdown Output via Gen AI:

Reference:

Template Pattern & Markdown | Coursera

Best Practices:

1. Saving Intermediate Files

Why: To prevent loss of progress, allow assembly of multiple outputs, and enable various directions based on intermediate files.

How: Regularly save files at different stages of the project, allowing for flexibility in assembling outputs or taking the idea in multiple directions. These act as safety nets, ensuring that no work is lost. If ChatGPT times out, provide it the files that you previously downloaded. You can also ask for a Zip file with all the files generated thus far. You can also take an intermediate output and iterate on it in multiple independent conversations by uploading it to each one.

2. Always Plan Step-by-Step

Why: For a seamless process that enhances repeatability.

How: Collaborate with ChatGPT to outline specific, detailed, and actionable steps. Save these plans for future reference or to restart a process, making the process more reliable. You can use human or AI planning, but having a step by step plan is key. Save plans as a file so that they are easier to repeat by uploading them at the start of a new conversation.

3. Use Mementos

Why: To have reminders of the state of the ongoing process.

How: Have ChatGPT create files listing the entire plan, the current step, and summarizing what has or hasn't been done. These mementos are different from the plan itself and act as a guide to the current state of the process. You can upload these to restart a process from a given point or help Code Interpreter remember where it is in the process.

4. Always Have ChatGPT Read and Explain Documents/Data

Why: To synchronize your understanding with GPT's understanding.

How: Request ChatGPT to explain what it reads in several ways and test its understanding by having it generate examples or explanations that demonstrate the concepts in the document. Make sure that you and Code Interpreter have the same understanding regarding the documents, data, etc. that you provide. Asking it to generate new examples of concepts is a great way to test reasoning.

5. Use Error Detection Methods

Why: To ensure consistency and accuracy.

How: Employ references to specific identifiers or quotations to ensure that the output is supported by the documents you provided. Build custom test cases with real or synthetic data to ensure outputs are consistent with the input documents. If you can't work with real data yet, generate synthetic data to test out a process and accuracy.

6. Ask ChatGPT to Try Alternate Approaches

Why: To overcome failure and make continuous progress.

How: If an approach fails, prompt ChatGPT to explore different strategies. Provide hints or ask it to plan, list, describe the rationale behind, and employ alternate methods.

7. Think of Prompts as Constraints

Why: For targeted and desired outputs.

How: Be explicit about your goals, requirements, and constraints. ChatGPT will try to generate any solution that fits, so the more specific you are, the closer the output will be to your needs. Don't ask for a fruit if you really want a green banana.

8. Edit the Conversation When Errors Occur

Why: To avoid error propagation.

How: Edit and correct any chat message that produces a bad output immediately and regenerate the output. Clean conversation histories are preferred to prevent introducing erroneous information that causes problems later.

9. Get Key Information into the Conversation

Why: To enhance reasoning and understanding.

How: Include all necessary information directly in the conversation so you and ChatGPT have a common understanding. Visibility of information makes reasoning more effective. If you can see the information in a recent chat message, Code Interpreter can as well.

10. Tell ChatGPT to Analyze Without Python When Needed

Why: Not all tasks require code.

How: If you are getting poor reasoning on unstructured text, direct ChatGPT to analyze, read, or summarize "without using Python code" or "manually". In most cases, you don't want Python code doing the textual analysis.

This guide walks you through every command and concept needed to build, from scratch and in plain English, a fully offline AI assistant using the Mistral 7B model that's production‑ready, secured in a sandbox, and tailored entirely to your company’s internal knowledge.



You’ll:

1. install Python, 
2. create an isolated environment, 
3. set up the Ollama runtime, 
4. download the Mistral model under its Apache 2.0 license, 
5. ingest your documents into text chunks, 
6. generate embeddings with a sentence-transformer, 
7. store them in a vector database (FAISS, Chroma, or Qdrant), 
8. wire everything together in a Retrieval‑Augmented Generation (RAG) pipeline using LangChain,
9. build a simple Streamlit web interface, 
10. containerize the whole app with Docker, and 
11. deploy it securely behind your firewall.
    
    
    

1. Install Python

First, you need the Python programming language on your computer:

1. Download Python:
   Go to the official downloads page and grab the installer for your operating system (Windows, macOS, or Linux). Python.org

2. Run the installer:

   • Windows/macOS: Launch the downloaded installer and follow the prompts.

   • Linux: You can install Python from your distribution’s package manager or compile from source.

3. Verify installation:
   Open a terminal (Command Prompt on Windows, Terminal on macOS/Linux) and type:

   bash
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   python --version
   

   You should see something like Python 3.13.3, which is the current stable release. Python.org

2. Create & Activate a Virtual Environment

Keeping dependencies isolated prevents conflicts with other software:

1. Create a project folder:

   bash
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   mkdir my_ai_project
   cd my_ai_project
   

2. Make a virtual environment using Python’s built‑in venv module:

   bash
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   python -m venv venv
   

   This creates a new folder venv/ containing its own Python interpreter and libraries. Python documentation

3. Activate the environment:

   • Windows:

     bash
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     venv\Scripts\activate
     

   • macOS/Linux:

     bash
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     source venv/bin/activate
     

   After activation, your prompt will show (venv) to indicate that you’re working inside this isolated environment. Python documentation

3. Install & Run Ollama (Sandboxed LLM Runtime)

Ollama provides a local CLI to host and interact with open‑source models offline:

1. Install Ollama by running their installer script:

   bash
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   curl -fsSL https://ollama.com/install.sh | sh
   

   This script detects your operating system and architecture, then installs the correct Ollama binary. Ollama

2. Start the Ollama service (runs in the background):

   bash
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   ollama serve
   

   This command launches a local server that can load and run models without internet access. GitHub

3. Check your installation:

   bash
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   ollama -v
   

   You should see the Ollama version printed, confirming it’s ready.

4. Pull & Test the Mistral Model

Mistral 7B is an Apache 2.0‑licensed model—no restrictions on military or commercial use:

1. Download Mistral 7B via Ollama:

   bash
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   ollama pull mistral
   

   Mistral is a 7.3 billion‑parameter model released under Apache 2.0, freely usable without restrictions. OllamaMistral AI | Frontier AI in your hands

2. Run a quick test:

   bash
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   ollama run mistral "Hello, Mistral!"
   

   You should see the model generate a completion for your prompt, confirming it works locally. Ollama

5. Ingest Documents & Generate Embeddings

To teach the AI your private data, you’ll convert documents into searchable vectors:

1. Install required Python libraries:

   bash
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   pip install sentence-transformers langchain
   

2. Load the embedding model in a Python script:

   python
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   from sentence_transformers import SentenceTransformer
   model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
   

   The all-MiniLM-L6-v2 model maps text to 384-dimensional vectors for semantic search. Hugging Face

3. Chunk your documents (e.g., split PDFs or text files into 500‑token pieces) and run:

   python
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   embeddings = model.encode(text_chunks)
   

   This produces one vector per chunk, ready for indexing.

6. Set Up a Local Vector Database

Store your embeddings so you can quickly find relevant text at query time:

1. FAISS (Facebook AI Similarity Search)
   Install the CPU‑only package:

   bash
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   pip install faiss-cpu
   

   FAISS can handle up to billions of vectors efficiently on a single machine. PyPI

2. Chroma (Apache 2.0 licensed)
   A lightweight embedding database with a simple Python client:

   bash
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   pip install chromadb
   

   Chroma makes it easy to spin up an embedding store in minutes. PyPI

3. Qdrant (Rust‑based, Docker‑friendly)
   Pull and run the Docker container:

   bash
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   docker pull qdrant/qdrant
   docker run -p 6333:6333 -p 6334:6334 qdrant/qdrant
   

   Qdrant offers filtering and payload storage alongside vector search. Qdrant - Vector Database

7. Build the Retrieval‑Augmented Generation (RAG) Pipeline

Combine embeddings search with the Mistral model to answer queries:

1. Install LangChain:

   bash
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   pip install langchain
   

   LangChain provides abstractions for embeddings, vectorstores, and LLM chaining. Introduction | 🦜️🔗 LangChain

2. Wire it together (example with FAISS):

   python
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   from langchain.embeddings import SentenceTransformerEmbeddings
   from langchain.vectorstores import FAISS
   from langchain.llms import Ollama
   from langchain import PromptTemplate, LLMChain
   
   embedder = SentenceTransformerEmbeddings('sentence-transformers/all-MiniLM-L6-v2')
   db = FAISS.from_texts(text_chunks, embedder)
   retriever = db.as_retriever()
   llm = Ollama(model="mistral")
   template = PromptTemplate.from_template(
       "Use the context below to answer the question.\n\nContext:\n{context}\n\nQuestion: {question}"
   )
   chain = LLMChain(llm=llm, prompt=template)
   
   def answer(question: str) -> str:
       docs = retriever.get_relevant_documents(question)
       context = "\n\n".join(d.page_content for d in docs)
       return chain.run(context=context, question=question)
   

   This function retrieves your top-k chunks, feeds them as context, and returns Mistral’s answer.

8. Create a Simple Streamlit Web Interface

Let non-technical users ask questions through a browser page:

1. Install Streamlit:

   bash
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   pip install streamlit
   

   Streamlit turns Python scripts into interactive web apps with minimal effort. Streamlit Docs

2. Write app.py:

   python
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   import streamlit as st
   from your_rag_module import answer
   
   st.title("Company AI Assistant")
   question = st.text_input("Ask a question about our documents:")
   if st.button("Submit"):
       response = answer(question)
       st.write(response)
   

3. Launch the app:

   bash
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   streamlit run app.py
   

   Your browser will open at http://localhost:8501, showing the chat interface. Streamlit Docs

9. Containerize & Deploy with Docker

Package your entire setup so it runs reliably anywhere:

1. Install Docker on Ubuntu (example):

   bash
   CopyEdit
   # Set up Docker’s official apt repository
   sudo apt-get update
   sudo apt-get install \
     ca-certificates \
     curl \
     gnupg \
     lsb-release
   curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo gpg --dearmor -o /usr/share/keyrings/docker-archive-keyring.gpg
   echo \
     "deb [arch=$(dpkg --print-architecture) signed-by=/usr/share/keyrings/docker-archive-keyring.gpg] \
     https://download.docker.com/linux/ubuntu \
     $(lsb_release -cs) stable" | sudo tee /etc/apt/sources.list.d/docker.list > /dev/null
   sudo apt-get update
   sudo apt-get install docker-ce docker-ce-cli containerd.io
   

   Docker Documentation

2. Enable non‑root Docker usage:

   bash
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   sudo usermod -aG docker $USER
   sudo reboot
   

   After reboot, you can run Docker commands without sudo. GeeksforGeeks

3. Create a Dockerfile in your project:

   dockerfile
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   FROM python:3.13-slim
   WORKDIR /app
   COPY venv/ venv/
   COPY . .
   ENV PATH="/app/venv/bin:$PATH"
   EXPOSE 8501
   CMD ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0"]
   

4. Build & run:

   bash
   CopyEdit
   docker build -t private-ai .
   docker run -d -p 8501:8501 private-ai
   

   Your app is now reachable at http://<server-ip>:8501 in any browser.

10. Secure & Maintain Your Deployment

• Run everything air‑gapped behind your corporate VPN or firewall.

• Implement role‑based access control (RBAC) or basic auth in front of Streamlit.

• Log queries and responses for auditing and to improve your data ingestion pipeline.

• Automate updates: schedule re-ingestion of new documents and re-indexing of embeddings.