submit work

Author: geemi725

_toc.yml

@@ -23,4 +23,5 @@ parts:
 
   - caption: "4. Resources"
     chapters:
-      - file: resources/glossary
+      - file: resources/glossary
+      - file: resources/submit

chapter_1/background.md

@@ -1,6 +1,6 @@
 # 1.1 Evolution of AI
 
-***This page provides background on AI. You may skip to Chapter 2 or Chapter 3 to see more hands-on applications.***
+***This page provides a background on AI. You may skip to Chapter 2 or Chapter 3 to see more hands-on applications.***
 
 From genome sequencing to single-cell transcriptomics to high-content imaging and protein structure prediction, modern biology generates data at a scale and complexity that exceeds traditional analytical approaches. Artificial intelligence (AI) has emerged as a powerful set of tools for extracting patterns, generating hypotheses, and accelerating discovery from biological data. 
 

chapter_2/llms_for_biology.ipynb

@@ -93,7 +93,7 @@
    },
    "outputs": [],
    "source": [
-    "!pip install openai pandas matplotlib requests datasets trl peft nltk rank_bm25 ipywidgets"
+    "!uv pip install openai pandas matplotlib requests datasets trl peft nltk rank_bm25 ipywidgets"
    ]
   },
   {
@@ -271,21 +271,19 @@
     "\n",
     "By combining information retrieval with language generation, RAG systems can reduce hallucinations, incorporate up-to-date knowledge, and provide responses grounded in real documents.\n",
     "\n",
+    "**Key steps in RAG:**\n",
+    "- Create/connect knowledge base\n",
+    "- Retrieving relevant documents\n",
+    "- Using a language model to generate insights\n",
+    "- Post-processing the results\n",
+    "\n",
     "<figure style=\"text-align: center;\">\n",
     "  <img src=\"https://raw.githubusercontent.com/Future-House/tutorial-series/main/figures/RAG.png\" \n",
     "       alt=\"rag_framework\" \n",
     "       style=\"max-width: 100%;\">\n",
     "  <figcaption><em>Figure 2.2.1: RAG Framework</em></figcaption>\n",
     "</figure>\n",
     "\n",
-    "\n",
-    "**Key steps:**\n",
-    "- Create/connect knowledge base\n",
-    "- Retrieving relevant documents\n",
-    "- Using a language model to generate insights\n",
-    "- Post-processing the results\n",
-    "\n",
-    "\n",
     "###  Step 1: Knowledge Base Construction\n",
     "\n",
     "First, we need access to a knowledge base. This could be a locally existing folder of PDF files or an online database. \n",
@@ -524,7 +522,7 @@
     "    for i, abs_text in enumerate(relevant_abstracts, 1):\n",
     "        print(f\"\\n--- Abstract {i} ---\\n{abs_text[:500]}...\")  # print first 500 chars\n",
     "\n",
-    "    print(\"\\nGenerating answer using OpenAI GPT-4...\\n\")\n",
+    "    print(f\"\\nGenerating answer using OpenAI {llm_model}...\\n\")\n",
     "    answer = generate_answer_with_openai(relevant_abstracts, query, llm_model)\n",
     "    print(\"=== Generated Answer ===\")\n",
     "    print(answer)\n",

chapter_2/uniprot_example.ipynb

@@ -66,7 +66,7 @@
    "outputs": [],
    "source": [
     "# Install the necessary packages\n",
-    "!pip install requests anthropic py3Dmol python-dotenv"
+    "!uv pip install requests anthropic py3Dmol python-dotenv"
    ]
   },
   {

index.md

@@ -23,10 +23,12 @@ By the end of this series, you should:
 
 - A curious mindset
 
-# Using Tutorials
+# How to Use the Tutorials
 
-The tutorials can be launched using the rocket (🚀) button  at the top of the page.
+The tutorials can be launched using the rocket (🚀) button  at the top of the page. 
 
+<details>
+<summary> 👇 Expand to see more detail  </summary>
 ### Option 1 — Google Colab (**recommended**)
 Opens the notebook in Google Colab with the fastest and most reliable experience.
 
@@ -57,9 +59,18 @@ ANTHROPIC_API_KEY=your_key_here
 - You only need API keys for the providers used in a given notebook.
 - Never commit or publicly share your API keys.
 - If a cell fails due to missing credentials, verify that your keys were loaded correctly before rerunning the cell.
+</details>
 
 
-# Contacting Us
+# Submit Your Work
+
+This tutorial series was created to help grow the scientific community’s engagement with AI and agentic workflows. If you enjoyed the series and built an agent, workflow, or related project, we’d love to see what you created.
+
+Project submissions are completely optional, but we welcome examples of experiments, tools, workflows, or extensions inspired by the tutorials.
+
+You can submit your work using this [Google Form](https://docs.google.com/forms/d/e/1FAIpQLSdgBk-ATooOA1teiqcqztAmBIQ_9jkEl4Ts5CDImV7KBdqA2Q/viewform?usp=publish-editor)
+
+# Provide Feedback
 
 We’d love to hear from you! Whether you run into issues, have ideas for improving the tutorials, or want to suggest new topics, feel free to reach out.