MIT-Emerging-Talent · AseelOmer · Dec 7, 2025 · Dec 7, 2025 · Dec 7, 2025 · Dec 7, 2025
diff --git a/2_open_source_models/quantized_models/mistral7b/readme.md b/2_open_source_models/quantized_models/mistral7b/readme.md
@@ -76,47 +76,3 @@ provided context.
 * the model, ideal for balancing performance and resource use)
 
 ---
-
-## 4. 📊 Project Results: Prompts & Responses
-
-Below is a sample of the prompts given to the RAG pipeline and the corresponding
- answers generated by the local Mistral 7B model.
-
-### Example 1: [Your Question Here]
-
-* **Prompt:**
-    > [**PASTE YOUR QUESTION HERE**]
-
-* **Generated Response:**
-    > [**PASTE THE MODEL'S RESPONSE HERE**]
-
-* **My Analysis:**
-    > [Add your notes]
-
----
-
-### Example 2: [Your Question Here]
-
-* **Prompt:**
-    > [**PASTE YOUR QUESTION HERE**]
-
-* **Generated Response:**
-    > [**PASTE THE MODEL'S RESPONSE HERE**]
-
-* **My Analysis:**
-    > [Add your notes here...]
-
----
-
-### Example 3: [Your Question Here]
-
-* **Prompt:**
-    > [**PASTE YOUR QUESTION HERE**]
-
-* **Generated Response:**
-    > [**PASTE THE MODEL'S RESPONSE HERE**]
-
-* **My Analysis:**
-    > [Add your notes here...]
-
----
diff --git a/qwen_model/README.md → ...en_source_models/slm/qwen_model/README.md b/qwen_model/README.md → ...en_source_models/slm/qwen_model/README.md
diff --git a/qwen_model/main.ipynb → ...n_source_models/slm/qwen_model/main.ipynb b/qwen_model/main.ipynb → ...n_source_models/slm/qwen_model/main.ipynb
diff --git a/qwen_model/output.md → ...en_source_models/slm/qwen_model/output.md b/qwen_model/output.md → ...en_source_models/slm/qwen_model/output.md
diff --git a/0_domain_study/rag_slm_study.md → 2_open_source_models/slm/rag_slm_study.md b/0_domain_study/rag_slm_study.md → 2_open_source_models/slm/rag_slm_study.md
diff --git a/5_communication_strategy/README.md b/5_communication_strategy/README.md
@@ -1 +1,28 @@
 # Communication Strategy
+
+📄 Why We Chose an Article as Our Communication Artifact
+
+For this project, we selected an article as our primary communication artifact because
+ it allowed us to clearly and meaningfully share the full story of our journey both
+  the technical and Thinking sides of it.
+
+An article gave us the space to explain:
+
+- Why we started this project and what motivated us to pursue it
+
+- The challenges we encountered, including the obstacles that slowed us down or
+ changed our direction
+
+- How we addressed these challenges, detailing the steps, decisions, and problem-solving
+ approaches we used
+
+- What we ultimately achieved, highlighting the key results and insights gained
+
+- The future work that is still needed, so others can build on what we started
+
+We chose this format because it is accessible, reflective, and allows readers to
+follow our thought process from beginning to end. More importantly, an article
+helps us communicate not only what we built, but why it mattered and how the
+experience shaped our learning.
+
+[🔗 Read the Article](https://your-article-link.com)
diff --git a/5_communication_strategy/guide.md b/5_communication_strategy/guide.md
diff --git a/6_final_presentation/README.md b/6_final_presentation/README.md
@@ -1 +1,14 @@
 # Final Presentation
+
+🟩 Design Explanation
+
+- We designed the presentation using a green color theme to reflect our focus on
+environmental cost awareness while testing the potential of open-source AI models.
+
+- The road-based layout was chosen intentionally it guides the audience through
+our journey step by step, showing where we encountered challenges, how we pivoted
+from those obstacles, and where we ultimately arrived by the end of the project.
+This design visually represents both our commitment to sustainability and the
+path we took to complete the work.
+
+  [🔗 Presentation](https://www.canva.com/design/DAG5zbv8oDw/hmVZKLfQVdhJZ7uVTn6Cug/edit)
diff --git a/6_final_presentation/guide.md b/6_final_presentation/guide.md
diff --git a/README.md b/README.md
@@ -120,14 +120,6 @@ A structured Google Form experiment collected:
 Outputs were randomized and anonymized to avoid bias. This provided a
 perception-based counterpart to technical evaluation.
 
-### 7. Analysing the Results
-
-....
-
-### 8. Publishing an Article
-
-....
-
 ---
 
 ## 📊 Key Findings
@@ -151,15 +143,6 @@ potential to compete meaningfully with commercial AI systems.
 
 ---
 
-## 🔮 Future Work
-
-- Evaluate additional open-source model families across diverse tasks
-- Test optimized pipelines in specialized domains (medical, legal, technical writing)
-- Track carbon footprint across full lifecycle (training to deployment)
-- Conduct ablation studies isolating RAG vs. recursive editing contributions
-
----
-
 ## 📢 Communication Strategy
 
 The research findings will be shared through formats designed for different
@@ -170,28 +153,30 @@ audiences and purposes:
 A comprehensive research article will document the complete experimental design,
 statistical analysis, and implications.
 
-🔗 **[View Aticle](link1)**
+🔗 **[View Article](./5_communication_strategy/README.md)**
 
 ### For Practitioners & Educators
 
 An executive presentation provides a visual overview of the research question,
 methodology, and key findings without requiring deep technical background.
 
-🔗 **[View Presentation](link2)**
+🔗 **[View Presentation](./6_final_presentation/README.md)**
 
 ### For the Community
 
 A public evaluation study invites participation in assessing AI-generated texts.
-This crowdsourced data forms a critical component of the research.
+This crowd sourced data forms a critical component of the research.
 
-🔗 **[Participate in Study](link3)**
+🔗 **[The Study](./3_experiment/survey_form.md)**
 
-### For Reproducibility
+---
 
-All materials (dataset, prompts, model outputs, evaluation scripts, and carbon
-tracking logs) are publicly available in this repository.
+## 🔮 Future Work
 
-🔗 **[Browse Repository](https://github.com/banuozyilmaz2-jpg/ELO2-GREEN-AI)**
+- Evaluate additional open-source model families across diverse tasks
+- Test optimized pipelines in specialized domains (medical, legal, technical writing)
+- Track carbon footprint across full lifecycle (training to deployment)
+- Conduct ablation studies isolating RAG vs. recursive editing contributions
 
 ---