This story is about how we built a Retrieval-Augmented Generation system for colorectal cancer survivors who needed daily, human-centered healing guidance. The Problem The Problem Most RAG systems are built to surface facts not feelings. They power search, chatbots, and enterprise Q&A. But what if your question isn’t just about “how,” but how to live again? how to live again? Let me tell you a story. A story of a survivor who dreamed, who aspired, who wanted so much from life. Yes, it’s me.It’s my story. I am a survivor. I survived the harsh battle against cancer. But when the treatment ended and the surgery was done, I thought I could go back to dreaming, aspiring, achieving… Only to learn that the battle wasn’t over yet. It had just changed shape. Most cancer survivors are told they're “done” after treatment. But for almost all of us, that's when the real struggle begins. As a colorectal cancer survivor living with LARS (Low Anterior Resection Syndrome), I face a relentless cycle of urgency, bloating, fatigue, and pain. Symptoms that aren’t just unpredictable. They are uncontrollable. LARS doesn’t just disrupt life. It erases it. I stopped eating normally. I stopped leaving my home. I stopped trusting my body. I amn’t living. I am existing. Dejected and depressed, I wanted to give up but I am determined to turn ‘setbacks’ to ‘comebacks’. I started my research to meet with people who are sailing in the same boat as mine. I heard their stories. One survivor said: “My legs are shaking now. No strength left. It’s been a week of constipation. I’m angry.” “My legs are shaking now. No strength left. It’s been a week of constipation. I’m angry.” Another shared: “I’ve had severe, nonstop diarrhea for days. Even my legs shake from the weakness. My BP is 69/54. I’m so exhausted.” “I’ve had severe, nonstop diarrhea for days. Even my legs shake from the weakness. My BP is 69/54. I’m so exhausted.” These extremes of incontinence or no stool at all trap survivors in a constant state of distress. And through it all, the world assumes we’ve recovered. Why the Tools We Have Aren’t Enough Why the Tools We Have Aren’t Enough I do what most survivors do. I track symptoms. I log food. I Google. I scroll through Facebook groups. I try meditation apps and fiber supplements. I even ask ChatGPT questions about bloating, diarrhea, constipation, fatigue. Sometimes, it answers kindly. Sometimes, it gives generic advice. But mostly, it doesn’t understand what I’m going through…not really. The tools we have today can track. They can inform. But they don’t guide. And they definitely don’t hold space for the emotional chaos, the fear, the identity loss that comes with every flare. I’m not looking for data.I’m looking for healing. And if no tool today can do that then I have to build one that will and as I already wrote in my earlier post, SANJEEVANI AI was born. earlier post From Information Retrieval to Recovery Guidance From Information Retrieval to Recovery Guidance In the world of AI, RAG (Retrieval-Augmented Generation) is used to fetch relevant documents so language models can generate more accurate answers. But survivors don’t need documents.We don’t need accuracy in the academic sense. We need resonance. resonance. So we reimagined RAG not as a search tool, but as a healing architecture. healing architecture Instead of pulling journal articles, we taught the system to retrieve: Survivor symptom logs
Food triggers and recovery routines
Reflections, emotional states, and pattern-matched outcomes Survivor symptom logs Food triggers and recovery routines Reflections, emotional states, and pattern-matched outcomes We trained it to search for what helped, not just what was written. And we didn’t stop there. We layered in a reasoning engine, one that not only recommends what to eat. For example, “If a user said, I feel constipated, it not only suggests “eat more fiber,” but explains why that may not work for someone with post-radiation bowel damage. why Through a deeply personal, survivor-trained RAG pipeline, we achieved the success. How Our the Healing Architecture Works How Our the Healing Architecture Works We designed SANJEEVANI AI with a simple but radical idea:healing isn’t a single answer — it’s an adaptable, daily flow. Traditional AI systems stop at response generation.But survivorship requires sensing, retrieving, reasoning, and explaining — all grounded in personal context. So we built a multi-layered architecture that does exactly that. 1. Smart Input Layer Smart Input Layer The journey begins when a survivor logs something:“Breakfast: oats with banana. Felt bloated. Urgency. Mood: anxious.” The journey begins when a survivor logs something:“Breakfast: oats with banana. Felt bloated. Urgency. Mood: anxious.” “Breakfast: oats with banana. Felt bloated. Urgency. Mood: anxious.” Whether it's voice or text, our interface converts that into 28+ structured data points including: Symptom severity
Emotional state
Food types
Energy levels
Bowel behavior (e.g., Bristol scale) Symptom severity Emotional state Food types Energy levels Bowel behavior (e.g., Bristol scale) We call this our Smart Logging System. It's voice-enabled, multilingual-ready, and designed for compassionate expression, not just data capture. 2. NLP + Metadata Extraction Layer NLP + Metadata Extraction Layer Once the log is captured, it flows into a BERT-based NLP layer, fine-tuned on: Colorectal cancer survivorship language
Symptom-event-emotion triplets
Layperson-to-clinical translation patterns Colorectal cancer survivorship language Symptom-event-emotion triplets Layperson-to-clinical translation patterns This layer extracts: Intent (e.g., looking for relief or routine validation)
Emotion (fear, frustration, confusion)
Temporal markers (e.g., post-meal, post-TAI, post-bowel movement) Intent (e.g., looking for relief or routine validation) Intent Emotion (fear, frustration, confusion) Emotion Temporal markers (e.g., post-meal, post-TAI, post-bowel movement) Temporal markers We enrich every log with metadata embeddings that describe more than what’s said, they infer what’s felt. 3. Agentic RAG Layer Agentic RAG Layer Here’s where our retrieval engine comes in but unlike traditional RAG, we don’t pull PDFs or papers. We retrieve: Curated survivor logs (anonymized)
Treatment-to-symptom maps
Food-to-impact relationships
Peer recommendations + emotional recovery timelines
Doctor-validated insights, tagged by symptom patterns Curated survivor logs (anonymized) Treatment-to-symptom maps Food-to-impact relationships Peer recommendations + emotional recovery timelines Doctor-validated insights, tagged by symptom patterns This data lives in a vector store, where each entry is embedded not just by keywords, but by: Symptom clusters
Emotional energy
Healing success likelihood Symptom clusters Emotional energy Healing success likelihood When a survivor logs a new entry, the system retrieves other cases that “feel” similar statistically, as well as experientially. 4. Hybrid Recommender Layer Hybrid Recommender Layer After retrieval, the system moves to recommendation following a ‘not one-size-fits-all’, but a hybrid approach: Content-based filtering suggests interventions aligned with the survivor’s past entries and current state
Collaborative filtering matches anonymized cohorts of similar survivors (age, symptom pattern, treatment type) Content-based filtering suggests interventions aligned with the survivor’s past entries and current state Content-based filtering Collaborative filtering matches anonymized cohorts of similar survivors (age, symptom pattern, treatment type) Collaborative filtering From this, SANJEEVANI AI chooses the most context-aware suggestion: A food change
A lifestyle routine
A mindset shift
Or just a calming “you’re doing okay” moment A food change A lifestyle routine A mindset shift Or just a calming “you’re doing okay” moment 5. QoLGPT Layer QoLGPT Layer Recommendations are one thing but how they’re explained is everything. Our QoLGPT layer is a fine-tuned GPT model trained on: Survivor storytelling
Peer-to-peer support threads
Emotionally regulated, non-clinical language
Compassionate, non-directive coaching tones Survivor storytelling Peer-to-peer support threads Emotionally regulated, non-clinical language Compassionate, non-directive coaching tones It explains whya certain meal may help.It validates fears without making assumptions.And most importantly, it doesn’t medicalize suffering, it meets it. why 6. Feedback Loop Feedback Loop Every interaction is logged not just for metrics alone, but mainly for system learning and adapts. If a survivor rejects a recommendation, or logs that something helped or hurt, the system adapts. Survivors  train our model by living rather than by tagging data. A New Kind of Intelligence for a New Kind of Healing A New Kind of Intelligence for a New Kind of Healing I didn’t set out to build an AI company. I set out to survive. And in that process, I discovered something that no spreadsheet, chatbot, or PDF could give me:the right insight at the right time with the right tone. Healing isn’t one decision. It’s a thousand tiny ones.When to eat. What to eat. Whether to walk. Whether to rest. Whether to try again after yesterday failed. What survivors need is not more content. We need a system that sees us not just our symptoms. That’s what SANJEEVANI AI is. That’s what SANJEEVANI AI is. An intelligent, adaptable, empathetic healing system trained on real human lives. It doesn’t diagnose or treat because that’s the medical oncologist’s job.It listens. It adapts. It guides. It’s not built to replace doctors. It’s built to support the space between the hours, days, and months when no one else is there but the pain, the food, the fear… and the survivor. We used RAG not to search. But to remember. We used GPT not to answer. But to explain. And we used our own stories as survivors not as anecdotes, but as data. Because the next generation of AI doesn’t need to be just intelligent. It needs to be kind.

This story contains new, firsthand information uncovered by the writer.

Aren

Guidance, Not Just Data: Redefining Survivor Recovery with AI

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