Last Updated on March 26, 2026 by Rajeev Bagra

Search is one of the most fundamental ideas in computer science. Whether you’re typing a query into a search engine, navigating through a game, or finding the fastest route between cities, you’re relying on some form of search algorithm.

In courses like CS50’s Introduction to Artificial Intelligence with Python, search is introduced as a core building block of intelligent systems. But not all search problems are the same.

👉 Official course link:
https://cs50.harvard.edu/ai/

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🧠 What Do We Mean by “Search” in Computer Science?

At its core, search is about:

Exploring a space of possibilities to find a solution.

But that “space” can look very different depending on the problem.

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🔎 Part 1: Text Search (Information Retrieval Systems)

When you search for something on a website or blog, you’re performing retrieval search.

💡 Problem Type

Find documents that match a query.

⚙️ How It Works

1. Content is preprocessed into an inverted index
   • Example:
     "python" → [doc1, doc5, doc20]
2. Your query is tokenized:
   • "learn python" → ["learn", "python"]
3. Matching documents are retrieved
4. Results are ranked using:
   • TF-IDF
   • BM25
   • Machine learning models

🎯 Key Idea

👉 You are not navigating through steps
👉 You are matching and ranking

📚 Learn More

• https://nlp.stanford.edu/IR-book/
• https://en.wikipedia.org/wiki/Okapi_BM25

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🧭 Part 2: Route Finding (Graph Search)

Now let’s shift to a completely different type of problem—finding the best route from Delhi to Mumbai.

💡 Problem Type

Find the optimal path from a start state to a goal state.

⚙️ How It Works

• Cities = Nodes
• Roads = Edges
• Distance/time = Cost

The system explores possible routes and chooses the best one.

🧮 Algorithms Used

• Dijkstra’s Algorithm
• A* Search Algorithm
• Breadth-First Search (BFS)
• Depth-First Search (DFS)

🎯 Key Idea

👉 You are building a path step-by-step
👉 The goal is optimality (shortest/fastest)

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🔄 Part 3: How CS50 AI Connects These Ideas

In CS50’s Introduction to Artificial Intelligence with Python, search is taught as a progression:

👉 Course homepage: https://cs50.harvard.edu/ai/

🪜 Step-by-Step Learning Path

1. Depth-First Search (DFS)

• Explores one path fully before backtracking
• Memory efficient
• Not guaranteed shortest path

2. Breadth-First Search (BFS)

• Explores level by level
• Guarantees shortest path (unweighted graphs)

3. Uniform Cost Search

• Expands lowest-cost path first

4. A* Search

• Uses:
  • Actual cost so far (g)
  • Estimated cost to goal (h)

👉 This is where intelligence enters search

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⚖️ Part 4: Text Search vs Route Search

Feature	Text Search	Route Search
Goal	Find relevant documents	Find optimal path
Data Structure	Inverted index	Graph
Output	Ranked list	Single best route
Nature	Approximate	Exact (often)
Algorithms	TF-IDF, BM25	BFS, Dijkstra, A*

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🤝 Part 5: The Deep Connection

Even though these problems seem different, they share powerful underlying ideas:

🧩 Shared Principles

• Both explore a search space
• Both use heuristics
• Both try to avoid brute-force computation

Example

• In text search → ranking = heuristic
• In A* → heuristic = estimated distance

👉 Both answer:

“What should I explore first to get the best result quickly?”

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🎮 Part 6: Beyond Text and Maps

Search algorithms power many real-world systems:

🎯 Applications

• Games (Chess, Tic-Tac-Toe) → Minimax
• Recommendation Systems → searching item space
• Predictive AI → exploring future outcomes
• Web Crawlers → navigating the web graph

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🚀 Final Takeaway

Search in computer science is not one thing—it’s a family of problem-solving strategies.

• 🔎 Text Search → Filter + Rank
• 🧭 Route Search → Explore + Optimize

And as you progress through structured learning like CS50 AI, you’ll see how simple search evolves into intelligent systems that power modern technology.

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