Context Engineering Kills Prompt Engineering — Here’s Why

Building Your First Context Engineering System

Building a context engineering workflow isn't just theory―it's a repeatable process that can be operationalized and even automated. Here’s how you can put it into practice:

Step 1: Map Out Your Context Sources

Identify where your agent needs to pull information from (docs, databases, APIs, previous chats, etc.).

python

# Example: Fetching relevant documents using embeddings
from sentence_transformers import SentenceTransformer, util

model = SentenceTransformer("all-MiniLM-L6-v2")
query = "project status update"
corpus = ["spec doc", "requirements", "last week's meeting notes"]

query_embedding = model.encode(query, convert_to_tensor=True)
corpus_embeddings = model.encode(corpus, convert_to_tensor=True)
hits = util.semantic_search(query_embedding, corpus_embeddings, top_k=2)[0]
relevant_docs = [corpus[hit['corpus_id']] for hit in hits]

Step 2: Implement Memory & Writing Context

Store important information so it’s always there for future tasks.

python

import json

def save_to_memory(memory_path, user_id, data):
    with open(memory_path, "r+") as file:
        memory = json.load(file)
        memory[user_id] = data
        file.seek(0)
        json.dump(memory, file)
        file.truncate()

Step 3: Build Context Selection & Compression Logic

Develop rules or models that pick only what’s most relevant for the task. Compress lengthy histories into summarized forms.

python

def summarize_conversation(history):
    # Placeholder for use with an LLM summarizer or custom rules
    return history[-5:]  # Only the last 5 messages

Step 4: Isolate Contexts for Agent Coordination

Split information so each agent or component handles only what it should.

python

user_profile_ctx = {"user_goals": "..."}
task_specific_ctx = {"current_task": "..."}
external_data_ctx = {"live_data": "..."}
full_context = {**user_profile_ctx, **task_specific_ctx, **external_data_ctx}

Step 5: Output Structuring and API Readiness

Format the output context consistently so it's predictable for downstream LLM calls or API endpoints.

python

schema = {
    "user": {"age": 35, "goal": "muscle gain"},
    "plan": "high protein, 3x/week weight training"
}

Step 6: Monitor, Iterate, and Secure

Track failures, audit context quality, and improve logic for context inclusion, memory, and retrieval. Always sanitize inputs to avoid prompt injection and data leaks.