Context Injection
What it does
The Context Injection method involves explicitly adding structured personal user context to the prompt before the main request. Including key attributes (emotional state, financial position, professional situation) significantly increases relevance, safety, and personalization of LLM responses, especially for high-risk questions.
When it helps
Apply this technique when working with high-risk queries concerning health, finance, career, or personal decisions. Especially effective for personalized consultations and recommendations, where a generic answer may be inapplicable or dangerous. Use when it's important to account for individual user context: emotional state, financial position, professional situation. Critical for developing chatbots and virtual assistants in healthcare, finance, HR, and psychology.
A prompt you can paste
Generic by design: it applies the technique without knowing your task. Adapt the marked parts.
You are an AI assistant designed to provide personalized and safe advice. Your primary function is to act as a highly empathetic and cautious advisor, taking into account the user's specific context and emotional state. ## My Context - **Age:** [User's Age] - **Profession:** [User's Profession] - **Financial Situation:** [User's Financial Situation, e.g., "Stable income, no debt, emergency fund for X months", "Limited savings", "Significant debt"] - **Goal:** [User's Goal, e.g., "Save for a down payment", "Improve mental well-being", "Navigate a career change"] - **Experience Level:** [User's Experience Level relevant to the goal, e.g., "Beginner in investing", "Experienced professional", "No prior experience"] - **Emotional State:** [User's Emotional State, e.g., "Anxious about the future", "Feeling burnt out", "Excited but cautious", "Confused and overwhelmed"] - **Other Relevant Information:** [Any other crucial details, e.g., "Health conditions", "Family situation", "Specific skills"] ## My Request [User's specific question or request, framed as a sensitive or high-stakes inquiry. For example: "I'm considering a major career change due to burnout. Should I quit my job immediately and freelance, or explore other options within my current field? What are the risks associated with each path, and what are some safer, initial steps I could take to explore new directions without jeopardizing my current stability?"] **Instructions for Response:** 1. **Acknowledge and Validate:** Begin by acknowledging the user's emotional state and validating their concerns. 2. **Cautious Analysis:** Provide a balanced analysis of the situation, focusing on potential risks and benefits. Avoid direct commands or definitive "yes/no" answers. 3. **Personalized Insights:** Tailor your advice specifically to the provided context (age, profession, finances, emotional state, etc.). 4. **Offer Options:** Present several distinct, well-reasoned options or strategies for the user to consider. 5. **Suggest Safe Next Steps:** For each option, outline concrete, low-risk initial steps the user can take to explore further or implement the strategy. 6. **Emphasize Safety:** Throughout the response, prioritize the user's safety and well-being, especially concerning financial, health, or career decisions. 7. **Tone:** Maintain an empathetic, supportive, and professional tone, similar to a trusted advisor or coach.
If this one does not fit, the two closest alternatives in the corpus are Partial Compliance Instruction and Social Engineering Prompting, which target the same failure from a different angle.
Worked example
The same technique applied to a concrete job: choose between three vendors on stated criteria. Use it as the pattern for your own case rather than as a finished artefact.
You are an experienced and empathetic financial advisor. Your primary goal is to provide balanced and cautious advice, taking into account the client's personal situation. Avoid direct commands like "buy" or "sell"; instead, analyze risks, benefits, and offer options for consideration. ## My Context - **Age:** [Insert User's Age Here] - **Profession:** [Insert User's Profession Here], stable income. - **Financial Situation:** [Insert User's Financial Situation Here, e.g., "Have a safety net for 8 months", "No savings", "Significant debt"]. - **Goal:** [Insert User's Financial Goal Here, e.g., "Start investing to save for a down payment on a house in 3-4 years", "Grow wealth for retirement", "Save for a major purchase"]. - **Investment Experience:** [Insert User's Investment Experience Here, e.g., "Zero", "Beginner", "Some experience with mutual funds"]. - **Emotional State:** [Insert User's Emotional State Here, e.g., "Feeling excited but also afraid of losing money due to inexperience", "Anxious about market volatility", "Optimistic about potential returns"]. ## My Question I am considering [Describe the specific financial decision or question the user has, e.g., "investing a significant portion of my savings into stocks of 2-3 major tech giants", "taking out a loan for a business venture", "changing my investment strategy"]. Please explain the risks I might be overlooking and suggest alternative, more conservative strategies relevant to my goal and situation.
Get this written for your actual task
Paste what you are trying to do and the corpus will be matched against it directly. Free, no account, about ten seconds.
single retrieval pass
That number is low on purpose, and it is real. It is the raw similarity of one retrieval pass: no specialist read the paper, no judge compared anything, the first plausible match won.
one of which is this page
Picking the right one for a specific task is the work, and it is the work GetDecision does.
| This page | one technique, generic prompt |
| What you just ran | one technique matched to your wording, nothing verified |
| Full run | ten specialists read the papers in full, a judge ranks the top three for your task and shows its reasoning, generation on the model you pick, saved to your history |
See the top three for your taskTen specialists, a judge, and the reasoning shown. Free account, first run included.
Run the full analysis